Duplicate from pearsonkyle/Sharp-onnx

Co-authored-by: Kyle Pearson <pearsonkyle@users.noreply.huggingface.co>

.gitattributes

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+sharp_fp16.onnx filter=lfs diff=lfs merge=lfs -text
+viewer.giff filter=lfs diff=lfs merge=lfs -text
+viewer.gif filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.DS_Store
+__pycache__/
+onnx__*
+monodepth_*
+feature_model*
+_Constant_*
+_init_model_*

README.md

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+---
+license: apple-amlr
+library_name: ml-sharp
+pipeline_tag: image-to-3d
+base_model: apple/Sharp
+tags:
+  - onnx
+  - monocular-view-synthesis
+  - gaussian-splatting
+  - quantization
+  - fp16
+---
+
+
+# Sharp Monocular View Synthesis in Less Than a Second (ONNX Edition)
+
+[![Project Page](https://img.shields.io/badge/Project-Page-green)](https://apple.github.io/ml-sharp/)
+[![arXiv](https://img.shields.io/badge/arXiv-2512.10685-b31b1b.svg)](https://arxiv.org/abs/2512.10685)
+
+
+This software project is a communnity contribution and not affiliated with the original the research paper: 
+
+
+> _Sharp Monocular View Synthesis in Less Than a Second_ by _Lars Mescheder, Wei Dong, Shiwei Li, Xuyang Bai, Marcel Santos, Peiyun Hu, Bruno Lecouat, Mingmin Zhen, Amaël Delaunoy, Tian Fang, Yanghai Tsin, Stephan Richter and Vladlen Koltun_.
+
+> We present SHARP, an approach to photorealistic view synthesis from a single image. Given a single photograph, SHARP regresses the parameters of a 3D Gaussian representation of the depicted scene. This is done in less than a second on a standard GPU via a single feedforward pass through a neural network. The 3D Gaussian representation produced by SHARP can then be rendered in real time, yielding high-resolution photorealistic images for nearby views. The representation is metric, with absolute scale, supporting metric camera movements.
+
+#### This release includes fully validated **ONNX** versions of SHARP (FP32 and FP16), optimized for cross-platform inference on Windows, Linux, and macOS.
+
+![](viewer.gif)
+
+Rendered using [Splat Viewer](https://huggingface.co/spaces/pearsonkyle/Gaussian-Splat-Viewer)
+
+## Getting started
+
+### 🚀 Run Inference
+
+Use the provided [inference_onnx.py](inference_onnx.py) script to run SHARP inference:
+
+```bash
+# Run inference with FP16 model (faster, smaller)
+python inference_onnx.py -m sharp_fp16.onnx -i test.png -o test.ply -d 0.5
+```
+
+**CLI Options:**
+- `-m, --model`: Path to ONNX model file
+- `-i, --input`: Path to input image (PNG, JPEG, etc.)
+- `-o, --output`: Path for output PLY file
+- `-d, --decimate`: Decimation ratio 0.0-1.0 (default: 1.0 = keep all)
+- `--disparity-factor`: Depth scale factor (default: 1.0)
+- `--depth-scale`: Depth exaggeration factor (default: 1.0)
+
+**Features:**
+- Cross-platform ONNX Runtime inference (CPU/GPU)
+- Automatic image preprocessing and resizing
+- Gaussian decimation for reduced file sizes
+- PLY output compatible with all major 3D Gaussian viewers
+
+## Model Input and Output
+
+### 📥 Input
+The ONNX model accepts two inputs:
+
+- **`image`**: A 3-channel RGB image in `float32` format with shape `(1, 3, H, W)`.
+  - Values expected in range `[0, 1]` (normalized RGB).
+  - Recommended resolution: `1536×1536` (matches training size).
+  - Aspect ratio preserved; input resized internally if needed.
+
+- **`disparity_factor`**: A scalar tensor of shape `(1,)` representing the ratio `focal_length / image_width`.
+  - Use `1.0` for standard cameras (e.g., typical smartphone or DSLR).
+  - Adjust to control depth scale: higher values = closer objects, lower values = farther scenes.
+
+### 📤 Output
+The model outputs five tensors representing a 3D Gaussian splat representation:
+
+| Output | Shape | Description |
+|--------|-------|-------------|
+| `mean_vectors_3d_positions` | `(1, N, 3)` | 3D positions in Normalized Device Coordinates (NDC) — x, y, z. |
+| `singular_values_scales` | `(1, N, 3)` | Scale parameters along each principal axis (width, height, depth). |
+| `quaternions_rotations` | `(1, N, 4)` | Unit quaternions `[w, x, y, z]` encoding orientation of each Gaussian. |
+| `colors_rgb_linear` | `(1, N, 3)` | Linear RGB color values in range `[0, 1]` (no gamma correction). |
+| `opacities_alpha_channel` | `(1, N)` | Opacity (alpha) values per Gaussian, in range `[0, 1]`. |
+
+The total number of Gaussians `N` is approximately 1,179,648 for the default model.
+
+## Model Conversion
+
+To convert SHARP from PyTorch to ONNX, use the provided conversion script:
+
+```bash
+# Convert to FP32 ONNX (higher precision)
+python convert_onnx.py -o sharp.onnx --validate
+
+# Convert to FP16 ONNX (faster inference, smaller model)
+python convert_onnx.py -o sharp_fp16.onnx -q fp16 --validate
+```
+
+**Conversion Options:**
+- `-c, --checkpoint`: Path to PyTorch checkpoint (downloads from Apple if not provided)
+- `-o, --output`: Output ONNX model path
+- `-q, --quantize`: Quantization type (`fp16` for half-precision)
+- `--validate`: Validate converted model against PyTorch reference
+- `--input-image`: Path to test image for validation
+
+**Requirements:**
+- PyTorch and ml-sharp source code (automatically downloaded)
+- ONNX and ONNX Runtime for validation
+
+## Citation
+
+If you find this work useful, please cite the original paper:
+
+```bibtex
+@inproceedings{Sharp2025:arxiv,
+  title      = {Sharp Monocular View Synthesis in Less Than a Second},
+  author     = {Lars Mescheder and Wei Dong and Shiwei Li and Xuyang Bai and Marcel Santos and Peiyun Hu and Bruno Lecouat and Mingmin Zhen and Ama\"{e}l Delaunoy and Tian Fang and Yanghai Tsin and Stephan R. Richter and Vladlen Koltun},
+  journal    = {arXiv preprint arXiv:2512.10685},
+  year       = {2025},
+  url        = {https://arxiv.org/abs/2512.10685},
+}
+```

convert_onnx.py

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+"""Convert SHARP PyTorch model to ONNX format."""
+
+from __future__ import annotations
+
+import argparse
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+import onnx
+import onnxruntime as ort
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from sharp.models import PredictorParams, create_predictor
+from sharp.models.predictor import RGBGaussianPredictor
+from sharp.utils import io
+
+LOGGER = logging.getLogger(__name__)
+DEFAULT_MODEL_URL = "https://ml-site.cdn-apple.com/models/sharp/sharp_2572gikvuh.pt"
+
+OUTPUT_NAMES = [
+    "mean_vectors_3d_positions",
+    "singular_values_scales",
+    "quaternions_rotations",
+    "colors_rgb_linear",
+    "opacities_alpha_channel",
+]
+
+
+@dataclass
+class ToleranceConfig:
+    random_tolerances: dict = None
+    image_tolerances: dict = None
+    angular_tolerances_random: dict = None
+    angular_tolerances_image: dict = None
+    # FP16-specific tolerances (looser due to reduced precision)
+    fp16_random_tolerances: dict = None
+    fp16_angular_tolerances_random: dict = None
+    fp16_image_tolerances: dict = None
+    fp16_angular_tolerances_image: dict = None
+
+    def __post_init__(self):
+        if self.random_tolerances is None:
+            self.random_tolerances = {
+                "mean_vectors_3d_positions": 0.001,
+                "singular_values_scales": 0.0001,
+                "quaternions_rotations": 2.0,  # Increased for ONNX numerical precision
+                "colors_rgb_linear": 0.002,
+                "opacities_alpha_channel": 0.005,
+            }
+        if self.image_tolerances is None:
+            self.image_tolerances = {
+                "mean_vectors_3d_positions": 3.5,
+                "singular_values_scales": 0.035,
+                "quaternions_rotations": 2.0,  # Increased for ONNX numerical precision
+                "colors_rgb_linear": 0.01,
+                "opacities_alpha_channel": 0.05,
+            }
+        if self.angular_tolerances_random is None:
+            self.angular_tolerances_random = {"mean": 0.01, "p99": 0.1, "p99_9": 1.0, "max": 10.0}
+        if self.angular_tolerances_image is None:
+            self.angular_tolerances_image = {"mean": 0.2, "p99": 2.0, "p99_9": 5.0, "max": 25.0}
+        # FP16 tolerances - much looser due to float16 precision (~3-4 decimal digits)
+        # These are empirically tuned based on actual FP16 vs FP32 differences
+        # Large models with many layers accumulate FP16 rounding errors
+        if self.fp16_random_tolerances is None:
+            self.fp16_random_tolerances = {
+                "mean_vectors_3d_positions": 20.0,  # Depth errors can be ~10 units for far objects
+                "singular_values_scales": 0.2,     # Scale can have ~0.16 max diff
+                "quaternions_rotations": 2.0,      # Validated separately via angular metrics
+                "colors_rgb_linear": 0.25,         # sRGB2linearRGB power func is precision-sensitive
+                "opacities_alpha_channel": 1.0,    # Opacity can have ~0.94 max diff
+            }
+        if self.fp16_angular_tolerances_random is None:
+            # Quaternion angular error is high due to accumulated FP16 precision loss
+            # 180 degree errors can occur when quaternion nearly flips sign
+            self.fp16_angular_tolerances_random = {"mean": 15.0, "p99": 75.0, "p99_9": 120.0, "max": 180.0}
+        # FP16 image tolerances - based on actual test.png validation results
+        if self.fp16_image_tolerances is None:
+            self.fp16_image_tolerances = {
+                "mean_vectors_3d_positions": 20.0,  # Observed ~18.3 max diff
+                "singular_values_scales": 0.3,      # Observed ~0.27 max diff
+                "quaternions_rotations": 2.0,       # Validated separately via angular metrics
+                "colors_rgb_linear": 0.25,          # sRGB2linearRGB power func is precision-sensitive
+                "opacities_alpha_channel": 1.0,     # Observed ~0.79 max diff
+            }
+        if self.fp16_angular_tolerances_image is None:
+            self.fp16_angular_tolerances_image = {"mean": 1.0, "p99": 10.0, "p99_9": 60.0, "max": 180.0}
+
+
+class QuaternionValidator:
+    def __init__(self, angular_tolerances=None, enable_outlier_analysis=True, outlier_thresholds=None):
+        self.angular_tolerances = angular_tolerances or {"mean": 0.01, "p99": 0.5, "p99_9": 2.0, "max": 15.0}
+        self.enable_outlier_analysis = enable_outlier_analysis
+        self.outlier_thresholds = outlier_thresholds or [5.0, 10.0, 15.0]
+
+    @staticmethod
+    def canonicalize_quaternion(q):
+        """Canonicalize quaternions by ensuring the largest-magnitude component is positive.
+        
+        This resolves the q/-q sign ambiguity. For edge cases where components have
+        similar magnitudes, we use a stable tie-breaking strategy.
+        """
+        abs_q = np.abs(q)
+        max_idx = np.argmax(abs_q, axis=-1, keepdims=True)
+        
+        # Get the value at the max index
+        max_val = np.take_along_axis(q, max_idx, axis=-1)
+        
+        # Flip sign if the largest component is negative
+        sign_flip = np.where(max_val < 0, -1.0, 1.0)
+        return q * sign_flip
+
+    @staticmethod
+    def compute_angular_differences(quats1, quats2):
+        """Compute angular differences between quaternion pairs.
+        
+        This accounts for the q/-q equivalence by taking the minimum angle
+        between the two possible orientations.
+        """
+        n1 = np.linalg.norm(quats1, axis=-1, keepdims=True)
+        n2 = np.linalg.norm(quats2, axis=-1, keepdims=True)
+        q1 = quats1 / np.clip(n1, 1e-12, None)
+        q2 = quats2 / np.clip(n2, 1e-12, None)
+        
+        # Compute dot product for both sign options
+        dots = np.sum(q1 * q2, axis=-1)
+        
+        # Use absolute value of dot product - handles sign ambiguity directly
+        # This is more robust than canonicalization which can fail at boundaries
+        dots = np.abs(dots)
+        dots = np.clip(dots, 0.0, 1.0)
+        ang_rad = 2.0 * np.arccos(dots)
+        ang_deg = np.degrees(ang_rad)
+        return ang_deg, {
+            "mean": float(np.mean(ang_deg)),
+            "std": float(np.std(ang_deg)),
+            "max": float(np.max(ang_deg)),
+            "p99": float(np.percentile(ang_deg, 99)),
+            "p99_9": float(np.percentile(ang_deg, 99.9)),
+        }
+
+    def validate(self, pt_quats, onnx_quats, image_name="Unknown"):
+        diff, stats = self.compute_angular_differences(pt_quats, onnx_quats)
+        passed = True
+        reasons = []
+        for k, t in self.angular_tolerances.items():
+            if k in stats and stats[k] > t:
+                passed = False
+                reasons.append(f"{k} angular {stats[k]:.4f} > {t:.4f}")
+        return {"image": image_name, "passed": passed, "failure_reasons": reasons, "stats": stats}
+
+
+class SharpModelTraceable(nn.Module):
+    def __init__(self, predictor):
+        super().__init__()
+        self.init_model = predictor.init_model
+        self.feature_model = predictor.feature_model
+        self.monodepth_model = predictor.monodepth_model
+        self.prediction_head = predictor.prediction_head
+        self.gaussian_composer = predictor.gaussian_composer
+        self.depth_alignment = predictor.depth_alignment
+
+    def forward(self, image, disparity_factor):
+        monodepth_out = self.monodepth_model(image)
+        disp = monodepth_out.disparity
+        disp_factor = disparity_factor[:, None, None, None]
+        disp_clamped = disp.clamp(min=1e-4, max=1e4)
+        depth = disp_factor / disp_clamped
+        depth, _ = self.depth_alignment(depth, None, monodepth_out.decoder_features)
+        init_out = self.init_model(image, depth)
+        feats = self.feature_model(init_out.feature_input, encodings=monodepth_out.output_features)
+        deltas = self.prediction_head(feats)
+        gaussians = self.gaussian_composer(deltas, init_out.gaussian_base_values, init_out.global_scale)
+        quats = gaussians.quaternions
+        # Normalize quaternions to unit length
+        qnorm = torch.sqrt(torch.clamp(torch.sum(quats * quats, dim=-1, keepdim=True), min=1e-12))
+        quats = quats / qnorm
+        # NOTE: We intentionally do NOT canonicalize quaternions here.
+        # Canonicalization (ensuring largest component is positive) uses argmax which is
+        # inherently unstable when components have similar magnitudes. With FP16, tiny
+        # precision differences can flip which component is "largest", causing 180° sign flips.
+        # Since q and -q represent the same rotation, renderers handle this correctly.
+        # Validation uses |dot product| to compare quaternions regardless of sign.
+        return (gaussians.mean_vectors, gaussians.singular_values, quats.float(), gaussians.colors, gaussians.opacities)
+
+
+# Ops that are numerically sensitive and should remain in FP32
+# These operations are critical for accurate depth estimation and Gaussian rendering
+FP16_OP_BLOCK_LIST = [
+    # Depth computation ops - critical for global_scale and depth normalization
+    'ReduceMin',     # Used in _rescale_depth to find min depth - critical for global_scale
+    'ReduceMax',     # May be used in depth clamping operations
+    'Div',           # Division (disparity_factor/depth, 1/depth_factor) accumulates errors
+    
+    # Activation functions - inverse depth uses softplus(inverse_softplus(a) + b)
+    'Softplus',      # Used in inverse depth activation - sensitive to small values
+    'Sigmoid',       # Used in inverse_softplus and scale activation
+    'Log',           # Used in inverse_softplus - can underflow near zero
+    'Exp',           # Used in various activations - can overflow
+    
+    # Arithmetic ops that amplify precision errors
+    'Reciprocal',    # 1/x is sensitive to precision for small x values
+    'Pow',           # Power operations amplify precision errors
+    'Sqrt',          # Square root in quaternion normalization
+    'Sub',           # Subtraction in normalizations can cause catastrophic cancellation
+    'Add',           # Addition in depth composition (inverse_softplus + delta)
+    'Mul',           # Multiplication for global_scale application - critical for depth
+    
+    # Normalization layers need FP32 for numerical stability
+    'ReduceMean',    # Used in normalization - needs FP32 precision
+    'LayerNormalization',  
+    'InstanceNormalization',
+    'BatchNormalization',
+    'GroupNormalization',  # Used extensively in UNet decoder
+    
+    # Clamp operations affect depth range computation
+    'Clip',          # Used in depth clamping (clamp(min=1e-4, max=1e4))
+    'Min',           # Element-wise min operations
+    'Max',           # Element-wise max operations
+    
+    # Shape/reshape ops that can affect tensor interpretations
+    'Flatten',       # Used in depth min computation
+    'Reshape',       # Can affect numerical precision during reshaping
+    
+    # Concatenation used in feature preparation
+    'Concat',        # Concatenating depth features
+]
+
+
+def remove_spurious_fp16_casts(model, blocked_node_names):
+    """Remove Cast nodes that convert blocked node outputs back to FP16.
+    
+    The float16 converter inserts Cast nodes at the boundary between FP32 and FP16
+    regions. For blocked nodes, it adds:
+    - Cast(input, to=FP32) before the blocked node
+    - Cast(output, to=FP16) after the blocked node
+    
+    The output Cast defeats our purpose since downstream ops then receive FP16 data.
+    This function removes the output Cast nodes and updates downstream references.
+    
+    Args:
+        model: ONNX model (modified in place)
+        blocked_node_names: List of node names that were blocked from FP16 conversion
+        
+    Returns:
+        Modified ONNX model
+    """
+    from onnx import TensorProto
+    
+    # Build set of blocked node name prefixes for matching Cast names
+    # Cast nodes are named like: /init_model/ReduceMin_output_cast0
+    blocked_prefixes = set()
+    for name in blocked_node_names:
+        # Extract prefix for matching cast nodes
+        # e.g., /init_model/ReduceMin -> matches /init_model/ReduceMin_output_cast0
+        blocked_prefixes.add(name)
+    
+    # Find Cast-to-FP16 nodes that follow blocked nodes
+    cast_nodes_to_remove = []
+    cast_output_mapping = {}  # Maps cast output to original output
+    
+    for node in model.graph.node:
+        if node.op_type == 'Cast':
+            # Check if this Cast outputs FP16
+            is_cast_to_fp16 = False
+            for attr in node.attribute:
+                if attr.name == 'to' and attr.i == TensorProto.FLOAT16:
+                    is_cast_to_fp16 = True
+                    break
+            
+            if is_cast_to_fp16:
+                # Check if this Cast is on the output of a blocked node
+                # Cast names follow the pattern: /original_node_name_output_cast0
+                cast_name = node.name
+                for prefix in blocked_prefixes:
+                    # Match patterns like:
+                    # Blocked: /init_model/ReduceMin
+                    # Cast: /init_model/ReduceMin_output_cast0
+                    if cast_name.startswith(prefix + '_output_cast'):
+                        cast_nodes_to_remove.append(node)
+                        # Map the cast output back to its input
+                        cast_output_mapping[node.output[0]] = node.input[0]
+                        break
+    
+    if not cast_nodes_to_remove:
+        LOGGER.info("  No spurious FP16 cast nodes found to remove")
+        return model
+    
+    LOGGER.info(f"  Removing {len(cast_nodes_to_remove)} spurious Cast-to-FP16 nodes")
+    
+    # Update all nodes that consume Cast outputs to consume the original outputs instead
+    for node in model.graph.node:
+        new_inputs = []
+        for inp in node.input:
+            if inp in cast_output_mapping:
+                new_inputs.append(cast_output_mapping[inp])
+            else:
+                new_inputs.append(inp)
+        # Clear and reassign inputs
+        del node.input[:]
+        node.input.extend(new_inputs)
+    
+    # Also update graph outputs if they reference cast outputs
+    for out in model.graph.output:
+        if out.name in cast_output_mapping:
+            out.name = cast_output_mapping[out.name]
+    
+    # Remove the Cast nodes from the graph
+    cast_names_to_remove = {n.name for n in cast_nodes_to_remove}
+    new_nodes = [n for n in model.graph.node if n.name not in cast_names_to_remove]
+    
+    # Clear and reassign nodes
+    del model.graph.node[:]
+    model.graph.node.extend(new_nodes)
+    
+    # Update value_info for the remapped tensors (change from FP16 to FP32)
+    for val in model.graph.value_info:
+        if val.name in cast_output_mapping.values():
+            # This tensor should remain FP32
+            val.type.tensor_type.elem_type = TensorProto.FLOAT
+    
+    return model
+
+
+def fix_depth_precision(model):
+    """Fix depth computation precision by ensuring FP32 flow through critical ops.
+    
+    The float16 converter inserts Cast nodes at FP32/FP16 boundaries, causing
+    depth values to undergo FP32→FP16→FP32 round-trips that lose precision.
+    
+    This function identifies and removes spurious FP16 Cast chains:
+    Cast(FP32->FP16) followed by Cast(FP16->FP32)
+    
+    These chains are lossy and can be replaced with direct FP32 connections.
+    """
+    from onnx import TensorProto
+    
+    # Build maps for efficient lookup
+    node_by_output = {}  # tensor_name -> node that produces it
+    consumers_by_input = {}  # tensor_name -> list of nodes that consume it
+    
+    for node in model.graph.node:
+        for out in node.output:
+            node_by_output[out] = node
+        for inp in node.input:
+            if inp not in consumers_by_input:
+                consumers_by_input[inp] = []
+            consumers_by_input[inp].append(node)
+    
+    # Find Cast-to-FP16 -> Cast-to-FP32 chains and remove them
+    # These are precision-losing round-trips
+    fp16_casts = []  # (cast_to_fp16_node, cast_to_fp32_node)
+    
+    for node in model.graph.node:
+        if node.op_type != 'Cast':
+            continue
+            
+        # Check if this is a Cast-to-FP16
+        is_to_fp16 = False
+        for attr in node.attribute:
+            if attr.name == 'to' and attr.i == TensorProto.FLOAT16:
+                is_to_fp16 = True
+                break
+        
+        if not is_to_fp16:
+            continue
+            
+        fp16_output = node.output[0]
+        fp32_input = node.input[0]
+        
+        # Check if the only consumer of this FP16 output is a Cast-to-FP32
+        consumers = consumers_by_input.get(fp16_output, [])
+        if len(consumers) != 1:
+            continue
+            
+        consumer = consumers[0]
+        if consumer.op_type != 'Cast':
+            continue
+            
+        is_to_fp32 = False
+        for attr in consumer.attribute:
+            if attr.name == 'to' and attr.i == TensorProto.FLOAT:
+                is_to_fp32 = True
+                break
+        
+        if is_to_fp32:
+            # Found a chain: Cast(FP32->FP16) -> Cast(FP16->FP32)
+            # The FP32 output of the second Cast should just use the original FP32 input
+            fp16_casts.append((node, consumer, fp32_input, consumer.output[0]))
+    
+    if not fp16_casts:
+        LOGGER.info("  No FP16 round-trip casts to fix")
+        return model
+    
+    LOGGER.info(f"  Found {len(fp16_casts)} FP16 round-trip cast chains to eliminate")
+    
+    # Build mapping from old output to new output (bypassing the chain)
+    output_mapping = {}  # old_fp32_output -> original_fp32_input
+    nodes_to_remove = set()
+    
+    for cast_to_fp16, cast_to_fp32, original_fp32, final_fp32 in fp16_casts:
+        output_mapping[final_fp32] = original_fp32
+        nodes_to_remove.add(cast_to_fp16.name)
+        nodes_to_remove.add(cast_to_fp32.name)
+    
+    # Update all nodes to use the original FP32 values instead of the round-tripped ones
+    for node in model.graph.node:
+        if node.name in nodes_to_remove:
+            continue
+        new_inputs = list(node.input)
+        for i, inp in enumerate(new_inputs):
+            if inp in output_mapping:
+                new_inputs[i] = output_mapping[inp]
+        del node.input[:]
+        node.input.extend(new_inputs)
+    
+    # Update graph outputs if they reference the round-tripped values
+    for out in model.graph.output:
+        if out.name in output_mapping:
+            LOGGER.info(f"  Updating graph output {out.name} -> {output_mapping[out.name]}")
+            out.name = output_mapping[out.name]
+    
+    # Remove the cast chain nodes
+    new_nodes = [n for n in model.graph.node if n.name not in nodes_to_remove]
+    del model.graph.node[:]
+    model.graph.node.extend(new_nodes)
+    
+    LOGGER.info(f"  Removed {len(nodes_to_remove)} Cast nodes from round-trip chains")
+    
+    return model
+
+
+def convert_to_onnx_fp16(
+    predictor: RGBGaussianPredictor,
+    output_path: Path,
+    input_shape: tuple = (1536, 1536),
+) -> Path:
+    """Convert SHARP model to ONNX with FP16 quantization.
+    
+    Uses ONNX-native post-export FP16 conversion which is faster and more reliable
+    than PyTorch-level quantization. The conversion:
+    - Keeps inputs/outputs as FP32 for compatibility with existing inference code
+    - Preserves numerically sensitive ops (Softplus, Log, Exp, etc.) in FP32
+    - Keeps init_model and gaussian_composer in FP32 for accurate depth scaling
+    - Converts compute-heavy ops (Conv, MatMul, etc.) to FP16 for speed
+    
+    Args:
+        predictor: The SHARP predictor model
+        output_path: Output path for ONNX model
+        input_shape: Input image shape (height, width)
+        
+    Returns:
+        Path to the exported ONNX model
+    """
+    # Import the onnxruntime.transformers float16 converter which works with paths
+    from onnxruntime.transformers.float16 import convert_float_to_float16
+    
+    LOGGER.info("Converting to ONNX with FP16 quantization (ONNX-native approach)...")
+    
+    # First export to FP32 ONNX using a temporary file
+    temp_fp32_path = output_path.parent / f"{output_path.stem}_temp_fp32.onnx"
+    
+    try:
+        # Export FP32 model first
+        LOGGER.info("Step 1/4: Exporting FP32 ONNX model...")
+        convert_to_onnx(predictor, temp_fp32_path, input_shape=input_shape, use_external_data=False)
+        
+        # Load the FP32 model to get node names for blocking
+        LOGGER.info("Step 2/4: Analyzing model and preparing node block list...")
+        model_fp32 = onnx.load(str(temp_fp32_path), load_external_data=True)
+        
+        # Build a node block list for nodes in critical paths:
+        # - /init_model/* : depth normalization and global_scale computation
+        # - /gaussian_composer/* : final Gaussian parameter composition with global_scale
+        # - Root-level depth/disparity ops: /Clip, /Div, /Mul that operate on depth
+        node_block_list = []
+        for node in model_fp32.graph.node:
+            node_name = node.name
+            # Block all init_model nodes (depth normalization, global_scale)
+            if '/init_model/' in node_name:
+                node_block_list.append(node_name)
+            # Block all gaussian_composer nodes (applies global_scale to outputs)
+            elif '/gaussian_composer/' in node_name:
+                node_block_list.append(node_name)
+            # Block ALL prediction_head nodes - quaternion/color/opacity deltas need FP32 precision
+            # FP16 precision loss here directly affects output quality
+            elif '/prediction_head/' in node_name:
+                node_block_list.append(node_name)
+            # Block feature_model decoder's final layers (feed into prediction_head)
+            elif '/feature_model/' in node_name and any(x in node_name for x in ['decoder/out', 'decoder/up_4', 'decoder/up_3']):
+                node_block_list.append(node_name)
+            # Block root-level ops that operate on depth (between monodepth and init_model)
+            elif node_name.startswith('/Clip') or node_name.startswith('/Div') or node_name.startswith('/Mul'):
+                node_block_list.append(node_name)
+            # Block final output processing ops (quaternion normalization)
+            elif node_name.startswith('/Sqrt') or node_name.startswith('/Clamp'):
+                node_block_list.append(node_name)
+            # Block Pow operations (used in sRGB2linearRGB conversion - power 2.4 is precision-sensitive)
+            elif 'Pow' in node_name:
+                node_block_list.append(node_name)
+        
+        LOGGER.info(f"  Blocking {len(node_block_list)} nodes from FP16 conversion")
+        if node_block_list:
+            LOGGER.info(f"  Sample blocked nodes: {node_block_list[:5]}...")
+        
+        # Clean up loaded model
+        del model_fp32
+        
+        # Convert to FP16 using ONNX-native conversion
+        # Use INVERSE APPROACH: Block ALL ops EXCEPT compute-heavy ones
+        # Only Conv, MatMul, Gemm get FP16 - everything else stays FP32
+        LOGGER.info("Step 3/4: Converting to FP16 (inverse approach - only compute ops)...")
+        
+        # Reload model for analysis
+        model_fp32 = onnx.load(str(temp_fp32_path), load_external_data=True)
+        
+        # Get all unique op types in the model
+        op_types_in_model = set()
+        for node in model_fp32.graph.node:
+            op_types_in_model.add(node.op_type)
+        
+        # Define ops that are SAFE for FP16 (compute-heavy, numerically stable)
+        FP16_SAFE_OPS = {'Conv', 'MatMul', 'Gemm', 'ConvTranspose'}
+        
+        # Block all ops EXCEPT the safe ones
+        op_block_list_all = list(op_types_in_model - FP16_SAFE_OPS)
+        
+        LOGGER.info(f"  Model has {len(op_types_in_model)} unique op types")
+        LOGGER.info(f"  FP16 ops: {FP16_SAFE_OPS & op_types_in_model}")
+        LOGGER.info(f"  FP32 ops: {len(op_block_list_all)} op types blocked")
+        
+        del model_fp32
+        
+        model_fp16 = convert_float_to_float16(
+            str(temp_fp32_path),  # Pass path string, not model object!
+            keep_io_types=True,   # Keep inputs/outputs as FP32
+            op_block_list=op_block_list_all,  # Block everything except compute ops
+            node_block_list=node_block_list,  # Still block critical nodes
+        )
+        
+        LOGGER.info(f"  Converted model has {len(model_fp16.graph.node)} nodes")
+        
+        # Post-process to fix the FP32 depth path
+        # Remove spurious FP16 casts that break the depth computation chain
+        model_fp16 = fix_depth_precision(model_fp16)
+        
+        LOGGER.info(f"  After depth precision fix: {len(model_fp16.graph.node)} nodes")
+        
+        # Clean up output path before saving
+        cleanup_onnx_files(output_path)
+        
+        # Save the FP16 model
+        LOGGER.info("Step 4/4: Saving FP16 model...")
+        onnx.save(model_fp16, str(output_path))
+        
+        # Report file size
+        if output_path.exists():
+            file_size_mb = output_path.stat().st_size / (1024**2)
+            LOGGER.info(f"FP16 ONNX model saved: {output_path} ({file_size_mb:.2f} MB)")
+            
+            # Compare with FP32 size
+            if temp_fp32_path.exists():
+                fp32_size_mb = temp_fp32_path.stat().st_size / (1024**2)
+                reduction = (1 - file_size_mb / fp32_size_mb) * 100
+                LOGGER.info(f"  Size reduction: {fp32_size_mb:.2f} MB -> {file_size_mb:.2f} MB ({reduction:.1f}% smaller)")
+        
+        return output_path
+        
+    finally:
+        # Clean up temporary FP32 file
+        cleanup_onnx_files(temp_fp32_path)
+
+
+def cleanup_onnx_files(onnx_path):
+    """Clean up ONNX model files including external data files."""
+    try:
+        if onnx_path.exists():
+            onnx_path.unlink()
+            #LOGGER.info(f"Removed {onnx_path}")
+    except Exception as e:
+        LOGGER.warning(f"Could not remove {onnx_path}: {e}")
+    
+    # Also clean up external data file with .onnx.data suffix
+    data_path = onnx_path.with_suffix('.onnx.data')
+    try:
+        if data_path.exists():
+            data_path.unlink()
+            #LOGGER.info(f"Removed {data_path}")
+    except Exception as e:
+        LOGGER.warning(f"Could not remove {data_path}: {e}")
+    
+    # Clean up any temporary files from conversion
+    temp_patterns = ["onnx__*", "monodepth_*", "feature_model*", "_Constant_*", "_init_model_*"]
+    import glob
+    for pattern in temp_patterns:
+        for f in glob.glob(pattern):
+            try:
+                Path(f).unlink()
+                #LOGGER.info(f"Removed temporary file {f}")
+            except Exception:
+                pass
+
+
+def cleanup_extraneous_files():
+    import glob
+    import os
+    patterns = ["onnx__*", "monodepth_*", "feature_model*", "_Constant_*", "_init_model_*"]
+    for p in patterns:
+        for f in glob.glob(p):
+            try:
+                os.remove(f)
+            except Exception:
+                pass
+
+
+def load_sharp_model(checkpoint_path=None):
+    if checkpoint_path is None:
+        LOGGER.info(f"Downloading model from {DEFAULT_MODEL_URL}")
+        state_dict = torch.hub.load_state_dict_from_url(DEFAULT_MODEL_URL, progress=True)
+    else:
+        LOGGER.info(f"Loading checkpoint from {checkpoint_path}")
+        state_dict = torch.load(checkpoint_path, weights_only=True, map_location="cpu")
+    predictor = create_predictor(PredictorParams())
+    predictor.load_state_dict(state_dict)
+    predictor.eval()
+    return predictor
+
+
+def convert_to_onnx(predictor, output_path, input_shape=(1536, 1536), use_external_data=True):
+    LOGGER.info("Exporting to ONNX format...")
+    predictor.depth_alignment.scale_map_estimator = None
+    model = SharpModelTraceable(predictor)
+    model.eval()
+    
+    LOGGER.info("Pre-warming model...")
+    with torch.no_grad():
+        for _ in range(3):
+            _ = model(torch.randn(1, 3, input_shape[0], input_shape[1]), torch.tensor([1.0]))
+    
+    cleanup_onnx_files(output_path)
+    
+    h, w = input_shape
+    torch.manual_seed(42)
+    example_image = torch.randn(1, 3, h, w)
+    example_disparity = torch.tensor([1.0])
+    
+    LOGGER.info(f"Exporting to ONNX: {output_path} (external_data={use_external_data})")
+    
+    dynamic_axes = {}
+    for name in OUTPUT_NAMES:
+        if name == "opacities_alpha_channel":
+            dynamic_axes[name] = {0: 'batch', 1: 'num_gaussians'}
+        else:
+            dynamic_axes[name] = {0: 'batch', 1: 'num_gaussians'}
+    
+    # For large models (>2GB), PyTorch ONNX export creates external data files
+    # regardless of the external_data flag. We always use external data during export
+    # and then optionally convert to a single file afterward.
+    temp_path = output_path.parent / f"{output_path.stem}_export_temp.onnx"
+    
+    torch.onnx.export(
+        model, (example_image, example_disparity), str(temp_path),
+        export_params=True, verbose=False,
+        input_names=['image', 'disparity_factor'],
+        output_names=OUTPUT_NAMES,
+        dynamic_axes=dynamic_axes,
+        opset_version=15,
+        # Always use external data for large models to avoid proto buffer limit
+        external_data=True,
+    )
+    
+    # Load and re-save with proper handling
+    LOGGER.info("Loading exported model and consolidating weights...")
+    model_proto = onnx.load(str(temp_path), load_external_data=True)
+    
+    # Clean up temp files before saving final output
+    cleanup_onnx_files(temp_path)
+    
+    if use_external_data:
+        # Save with external data file
+        data_path = output_path.with_suffix('.onnx.data')
+        onnx.save_model(
+            model_proto, 
+            str(output_path),
+            save_as_external_data=True,
+            all_tensors_to_one_file=True,
+            location=data_path.name,
+            size_threshold=0,  # Save all tensors externally
+        )
+        if data_path.exists():
+            data_size_gb = data_path.stat().st_size / (1024**3)
+            LOGGER.info(f"External data file saved: {data_path} ({data_size_gb:.2f} GB)")
+    else:
+        # For models >2GB, we must use external data due to protobuf limits
+        # Check estimated size and force external data if needed
+        estimated_size = sum(t.ByteSize() if hasattr(t, 'ByteSize') else 0 for t in model_proto.graph.initializer)
+        if estimated_size > 2 * 1024**3:  # 2GB limit
+            LOGGER.info("Model exceeds 2GB protobuf limit, using external data format...")
+            data_path = output_path.with_suffix('.onnx.data')
+            onnx.save_model(
+                model_proto, 
+                str(output_path),
+                save_as_external_data=True,
+                all_tensors_to_one_file=True,
+                location=data_path.name,
+                size_threshold=0,
+            )
+            if data_path.exists():
+                data_size_gb = data_path.stat().st_size / (1024**3)
+                LOGGER.info(f"External data file saved: {data_path} ({data_size_gb:.2f} GB)")
+        else:
+            # Convert external data to internal (inline) - this works for models <2GB
+            try:
+                onnx.save_model(model_proto, str(output_path))
+                file_size_gb = output_path.stat().st_size / (1024**3)
+                LOGGER.info(f"Inline model saved: {file_size_gb:.2f} GB")
+            except Exception as e:
+                LOGGER.warning(f"Could not save inline model: {e}")
+                LOGGER.info("Falling back to external data format...")
+                data_path = output_path.with_suffix('.onnx.data')
+                onnx.save_model(
+                    model_proto, 
+                    str(output_path),
+                    save_as_external_data=True,
+                    all_tensors_to_one_file=True,
+                    location=data_path.name,
+                    size_threshold=0,
+                )
+    
+    LOGGER.info(f"ONNX model saved to {output_path}")
+    return output_path
+
+
+def find_onnx_output_key(name, onnx_outputs):
+    if name in onnx_outputs:
+        return name
+    for key in onnx_outputs:
+        if name.split('_')[0] in key.lower():
+            return key
+    return list(onnx_outputs.keys())[OUTPUT_NAMES.index(name) if name in OUTPUT_NAMES else 0]
+
+
+def load_and_preprocess_image(image_path, target_size=(1536, 1536)):
+    LOGGER.info(f"Loading image from {image_path}")
+    image_np, orig_size, f_px = io.load_rgb(image_path)
+    # Fallback to getting size from array if orig_size is None
+    if orig_size is None:
+        orig_size = (image_np.shape[1], image_np.shape[0])
+    LOGGER.info(f"Original size: {orig_size}, focal: {f_px:.2f}px")
+    tensor = torch.from_numpy(image_np.copy()).float() / 255.0
+    tensor = tensor.permute(2, 0, 1)
+    if (orig_size[0], orig_size[1]) != (target_size[1], target_size[0]):
+        LOGGER.info(f"Resizing to {target_size[1]}x{target_size[0]}")
+        tensor = F.interpolate(tensor.unsqueeze(0), size=target_size, mode="bilinear", align_corners=True).squeeze(0)
+    tensor = tensor.unsqueeze(0)
+    LOGGER.info(f"Preprocessed shape: {tensor.shape}, range: [{tensor.min():.4f}, {tensor.max():.4f}]")
+    return tensor, f_px, orig_size
+
+
+def run_inference_pair(pytorch_model, onnx_path, image_tensor, disparity_factor=1.0, log_internals=False):
+    wrapper = SharpModelTraceable(pytorch_model)
+    wrapper.eval()
+    image_tensor = image_tensor.float()
+    disp_pt = torch.tensor([disparity_factor], dtype=torch.float32)
+    with torch.no_grad():
+        pt_outputs = wrapper(image_tensor, disp_pt)
+    
+    pt_np = [o.numpy() for o in pt_outputs]
+    
+    session = ort.InferenceSession(str(onnx_path), providers=['CPUExecutionProvider'])
+    onnx_inputs = {"image": image_tensor.numpy(), "disparity_factor": np.array([disparity_factor], dtype=np.float32)}
+    onnx_raw = session.run(None, onnx_inputs)
+    
+    LOGGER.info(f"ONNX raw outputs count: {len(onnx_raw)}, first shape: {onnx_raw[0].shape if len(onnx_raw) > 0 else 'N/A'}")
+    
+    # Check if outputs are already separated
+    if len(onnx_raw) == 5:
+        # ONNX returns separate outputs
+        onnx_splits = list(onnx_raw)
+    elif len(onnx_raw) == 1:
+        # ONNX returns concatenated output - split it
+        total_size = onnx_raw[0].shape[-1]
+        LOGGER.info(f"ONNX single output total size: {total_size}")
+        
+        # Cumulative sizes: positions(3) + scales(3) + quats(4) + colors(3) + opacities(1) = 14
+        sizes = [3, 3, 4, 3, 1]
+        start = 0
+        onnx_splits = []
+        for i, size in enumerate(sizes):
+            onnx_splits.append(onnx_raw[0][:, :, start:start+size])
+            start += size
+    else:
+        onnx_splits = list(onnx_raw)
+    
+    return pt_np, onnx_splits
+
+
+def format_validation_table(results, image_name="", include_image=False):
+    lines = []
+    if include_image:
+        lines.append("| Image | Output | Max Diff | Mean Diff | P99 Diff | Status |")
+        lines.append("|-------|--------|----------|-----------|----------|--------|")
+        for r in results:
+            name = r["output"].replace("_", " ").title()
+            status = "PASS" if r["passed"] else "FAIL"
+            lines.append(f"| {image_name} | {name} | {r['max_diff']} | {r['mean_diff']} | {r['p99_diff']} | {status} |")
+    else:
+        lines.append("| Output | Max Diff | Mean Diff | P99 Diff | Status |")
+        lines.append("|--------|----------|-----------|----------|--------|")
+        for r in results:
+            name = r["output"].replace("_", " ").title()
+            status = "PASS" if r["passed"] else "FAIL"
+            lines.append(f"| {name} | {r['max_diff']} | {r['mean_diff']} | {r['p99_diff']} | {status} |")
+    return "\n".join(lines)
+
+
+def validate_with_image(onnx_path, pytorch_model, image_path, input_shape=(1536, 1536), is_fp16_model=False):
+    LOGGER.info(f"Validating with image: {image_path}")
+    test_image, f_px, (w, h) = load_and_preprocess_image(image_path, input_shape)
+    disparity_factor = f_px / w
+    LOGGER.info(f"Using disparity_factor = {disparity_factor:.6f}")
+    
+    pt_outputs, onnx_out = run_inference_pair(pytorch_model, onnx_path, test_image, disparity_factor)
+    
+    LOGGER.info(f"PyTorch outputs shapes: {[o.shape for o in pt_outputs]}")
+    LOGGER.info(f"ONNX output shapes: {[o.shape for o in onnx_out]}")
+    
+    tolerance_config = ToleranceConfig()
+    if is_fp16_model:
+        tolerances = tolerance_config.fp16_image_tolerances
+        quat_validator = QuaternionValidator(angular_tolerances=tolerance_config.fp16_angular_tolerances_image)
+        LOGGER.info("Using FP16 validation tolerances (comparing FP16 ONNX vs FP32 PyTorch reference)")
+    else:
+        tolerances = tolerance_config.image_tolerances
+        quat_validator = QuaternionValidator(angular_tolerances=tolerance_config.angular_tolerances_image)
+    
+    all_passed = True
+    results = []
+    
+    for i, name in enumerate(OUTPUT_NAMES):
+        pt_out = pt_outputs[i]
+        onnx_output = onnx_out[i]
+        
+        result = {"output": name, "passed": True, "failure_reason": ""}
+        
+        if name == "quaternions_rotations":
+            quat_result = quat_validator.validate(pt_out, onnx_output, image_path.name)
+            result.update({
+                "max_diff": f"{quat_result['stats']['max']:.6f}",
+                "mean_diff": f"{quat_result['stats']['mean']:.6f}",
+                "p99_diff": f"{quat_result['stats']['p99']:.6f}",
+                "passed": quat_result["passed"],
+                "failure_reason": "; ".join(quat_result["failure_reasons"]),
+            })
+            if not quat_result["passed"]:
+                all_passed = False
+        else:
+            diff = np.abs(pt_out - onnx_output)
+            tol = tolerances.get(name, 0.01)
+            result.update({
+                "max_diff": f"{np.max(diff):.6f}",
+                "mean_diff": f"{np.mean(diff):.6f}",
+                "p99_diff": f"{np.percentile(diff, 99):.6f}",
+            })
+            if np.max(diff) > tol:
+                result["passed"] = False
+                result["failure_reason"] = f"max diff {np.max(diff):.6f} > tol {tol:.6f}"
+                all_passed = False
+        
+        results.append(result)
+    
+    LOGGER.info(f"\n### Validation Results: {image_path.name}\n")
+    LOGGER.info(format_validation_table(results, image_path.name, include_image=True))
+    LOGGER.info("")
+    
+    return all_passed
+
+
+def validate_onnx_model(onnx_path, pytorch_model, input_shape=(1536, 1536), angular_tolerances=None, is_fp16_model=False):
+    LOGGER.info("Validating ONNX model against PyTorch...")
+    np.random.seed(42)
+    torch.manual_seed(42)
+    
+    # Always use FP32 inputs - FP16 models with keep_io_types=True accept FP32 inputs
+    # and we compare against FP32 PyTorch reference for meaningful accuracy measurement
+    test_image_np = np.random.rand(1, 3, input_shape[0], input_shape[1]).astype(np.float32)
+    test_disp_np = np.array([1.0], dtype=np.float32)
+    
+    # Create a wrapper for PyTorch model - always use FP32 as reference
+    wrapper = SharpModelTraceable(pytorch_model)
+    wrapper.eval()
+    
+    test_image = torch.from_numpy(test_image_np)
+    test_disp = torch.from_numpy(test_disp_np)
+    
+    with torch.no_grad():
+        pt_out = wrapper(test_image, test_disp)
+    
+    # ONNX inference - always use FP32 inputs (FP16 model handles conversion internally)
+    session = ort.InferenceSession(str(onnx_path), providers=['CPUExecutionProvider'])
+    onnx_raw = session.run(None, {"image": test_image_np, "disparity_factor": test_disp_np})
+    
+    # Use same splitting logic as run_inference_pair
+    if len(onnx_raw) == 5:
+        onnx_splits = list(onnx_raw)
+    elif len(onnx_raw) == 1:
+        sizes = [3, 3, 4, 3, 1]
+        start = 0
+        onnx_splits = []
+        for size in sizes:
+            onnx_splits.append(onnx_raw[0][:, :, start:start+size])
+            start += size
+    else:
+        onnx_splits = list(onnx_raw)
+    
+    tolerance_config = ToleranceConfig()
+    # Use FP16 tolerances if validating FP16 model (compared against FP32 PyTorch reference)
+    if is_fp16_model:
+        tolerances = tolerance_config.fp16_random_tolerances
+        quat_validator = QuaternionValidator(angular_tolerances=angular_tolerances or tolerance_config.fp16_angular_tolerances_random)
+        LOGGER.info("Using FP16 validation tolerances (comparing FP16 ONNX vs FP32 PyTorch reference)")
+    else:
+        tolerances = tolerance_config.random_tolerances
+        quat_validator = QuaternionValidator(angular_tolerances=angular_tolerances or tolerance_config.angular_tolerances_random)
+    
+    all_passed = True
+    results = []
+    
+    for i, name in enumerate(OUTPUT_NAMES):
+        pt_o = pt_out[i].numpy()
+        onnx_o = onnx_splits[i]
+        result = {"output": name, "passed": True, "failure_reason": ""}
+        
+        if name == "quaternions_rotations":
+            qr = quat_validator.validate(pt_o, onnx_o, "Random")
+            result.update({
+                "max_diff": f"{qr['stats']['max']:.6f}",
+                "mean_diff": f"{qr['stats']['mean']:.6f}",
+                "p99_diff": f"{qr['stats']['p99']:.6f}",
+                "passed": qr["passed"],
+                "failure_reason": "; ".join(qr["failure_reasons"]),
+            })
+            if not qr["passed"]:
+                all_passed = False
+        else:
+            diff = np.abs(pt_o - onnx_o)
+            tol = tolerances.get(name, 0.01)
+            result.update({
+                "max_diff": f"{np.max(diff):.6f}",
+                "mean_diff": f"{np.mean(diff):.6f}",
+                "p99_diff": f"{np.percentile(diff, 99):.6f}",
+            })
+            if np.max(diff) > tol:
+                result["passed"] = False
+                result["failure_reason"] = f"max diff {np.max(diff):.6f} > tol {tol:.6f}"
+                all_passed = False
+        
+        results.append(result)
+    
+    LOGGER.info("\n### Random Validation Results\n")
+    LOGGER.info(format_validation_table(results))
+    LOGGER.info("")
+    
+    return all_passed
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Convert SHARP PyTorch model to ONNX format")
+    parser.add_argument("-c", "--checkpoint", type=Path, default=None, help="Path to PyTorch checkpoint")
+    parser.add_argument("-o", "--output", type=Path, default=Path("sharp.onnx"), help="Output path for ONNX model")
+    parser.add_argument("-q", "--quantize", type=str, default=None, choices=["fp16"], help="Quantization type (fp16 for float16)")
+    parser.add_argument("--height", type=int, default=1536, help="Input image height")
+    parser.add_argument("--width", type=int, default=1536, help="Input image width")
+    parser.add_argument("--validate", action="store_true", help="Validate ONNX model against PyTorch")
+    parser.add_argument("-v", "--verbose", action="store_true", help="Enable verbose logging")
+    parser.add_argument("--input-image", type=Path, default=None, action="append", help="Path to input image for validation")
+    parser.add_argument("--no-external-data", action="store_true", help="Save model with inline data (no .onnx.data file needed)")
+    parser.add_argument("--tolerance-mean", type=float, default=None, help="Custom mean angular tolerance for quaternion validation")
+    parser.add_argument("--tolerance-p99", type=float, default=None, help="Custom p99 angular tolerance for quaternion validation")
+    parser.add_argument("--tolerance-max", type=float, default=None, help="Custom max angular tolerance for quaternion validation")
+    
+    args = parser.parse_args()
+    
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO,
+                       format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+    
+    LOGGER.info("Loading SHARP model...")
+    predictor = load_sharp_model(args.checkpoint)
+    
+    input_shape = (args.height, args.width)
+    
+    LOGGER.info(f"Converting to ONNX: {args.output}")
+    
+    # Handle quantization
+    if args.quantize == "fp16":
+        LOGGER.info("Using FP16 quantization (ONNX-native post-export conversion)...")
+        convert_to_onnx_fp16(
+            predictor, 
+            args.output, 
+            input_shape=input_shape,
+        )
+    else:
+        # Standard float32 conversion
+        convert_to_onnx(predictor, args.output, input_shape=input_shape, use_external_data=False)
+    
+    LOGGER.info(f"ONNX model saved to {args.output}")
+    
+    is_fp16 = args.quantize == "fp16"
+    
+    if args.validate:
+        if args.input_image:
+            for img_path in args.input_image:
+                if not img_path.exists():
+                    LOGGER.error(f"Image not found: {img_path}")
+                    return 1
+                passed = validate_with_image(args.output, predictor, img_path, input_shape, is_fp16_model=is_fp16)
+                if not passed:
+                    LOGGER.error(f"Validation failed for {img_path}")
+                    return 1
+        else:
+            angular_tolerances = None
+            if args.tolerance_mean or args.tolerance_p99 or args.tolerance_max:
+                angular_tolerances = {
+                    "mean": args.tolerance_mean if args.tolerance_mean else 0.01,
+                    "p99": args.tolerance_p99 if args.tolerance_p99 else 0.5,
+                    "p99_9": 2.0,
+                    "max": args.tolerance_max if args.tolerance_max else 15.0,
+                }
+            # Use FP16 tolerances for FP16 model validation (still uses FP32 inputs)
+            is_fp16_model = args.quantize == "fp16"
+            passed = validate_onnx_model(args.output, predictor, input_shape, angular_tolerances=angular_tolerances, is_fp16_model=is_fp16_model)
+            if passed:
+                LOGGER.info("Validation passed!")
+            else:
+                LOGGER.error("Validation failed!")
+                return 1
+    
+    cleanup_extraneous_files()
+    LOGGER.info("Conversion complete!")
+    return 0
+
+
+if __name__ == "__main__":
+    exit(main())

inference_onnx.py

@@ -0,0 +1,332 @@
+#!/usr/bin/env python3
+"""ONNX Inference Script for SHARP Model.
+
+Loads an ONNX model (fp32 or fp16), runs inference on an input image,
+and exports the result as a PLY file.
+
+Usage:
+    # Convert and validate FP16 model
+    python convert_onnx.py -o sharp_fp16.onnx -q fp16 --validate
+
+    # Run inference with FP16 model
+    python inference_onnx.py -m sharp_fp16.onnx -i test.png -o test.ply -d 0.5
+"""
+
+from __future__ import annotations
+
+import argparse
+import logging
+from pathlib import Path
+
+import numpy as np
+import onnxruntime as ort
+from PIL import Image
+from plyfile import PlyData, PlyElement
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+LOGGER = logging.getLogger(__name__)
+
+DEFAULT_HEIGHT = 1536
+DEFAULT_WIDTH = 1536
+
+
+def linear_to_srgb(linear: float) -> float:
+    if linear <= 0.0031308:
+        return linear * 12.92
+    return 1.055 * pow(linear, 1.0 / 2.4) - 0.055
+
+
+def rgb_to_sh(rgb: float) -> float:
+    coeff_degree0 = 1.0 / np.sqrt(4.0 * np.pi)
+    return (rgb - 0.5) / coeff_degree0
+
+
+def inverse_sigmoid(x: float) -> float:
+    x = np.clip(x, 1e-6, 1.0 - 1e-6)
+    return np.log(x / (1.0 - x))
+
+
+def preprocess_image(image_path: str | Path, target_size: tuple[int, int] = (DEFAULT_HEIGHT, DEFAULT_WIDTH)):
+    """Load and preprocess an image for ONNX inference."""
+    image_path = Path(image_path)
+    target_h, target_w = target_size
+
+    img = Image.open(image_path)
+    original_size = img.size
+    focal_length_px = original_size[0]
+
+    if img.size != (target_w, target_h):
+        img = img.resize((target_w, target_h), Image.BILINEAR)
+
+    img_np = np.array(img, dtype=np.float32) / 255.0
+
+    if img_np.shape[2] == 4:
+        img_np = img_np[:, :, :3]
+
+    img_np = np.transpose(img_np, (2, 0, 1))
+    img_np = np.expand_dims(img_np, axis=0)
+
+    LOGGER.info(f"Loaded image: {image_path}, original size: {original_size}")
+    LOGGER.info(f"Preprocessed shape: {img_np.shape}, range: [{img_np.min():.4f}, {img_np.max():.4f}]")
+
+    return img_np, float(focal_length_px), original_size
+
+
+def run_inference(onnx_path: str | Path, image: np.ndarray, disparity_factor: float = 1.0) -> dict[str, np.ndarray]:
+    """Run ONNX inference on the preprocessed image."""
+    onnx_path = Path(onnx_path)
+
+    LOGGER.info(f"Loading ONNX model: {onnx_path}")
+    
+    # Configure session to suppress constant folding warnings for FP16 ops
+    # These warnings are benign - FP16 Sqrt/Tile ops run correctly but can't be pre-folded
+    sess_options = ort.SessionOptions()
+    sess_options.log_severity_level = 3  # 0=Verbose, 1=Info, 2=Warning, 3=Error, 4=Fatal
+    
+    # Use CPUExecutionProvider for universal compatibility
+    # Works on all platforms and handles large models with external data files
+    session = ort.InferenceSession(str(onnx_path), sess_options, providers=['CPUExecutionProvider'])
+    LOGGER.info("Using CPUExecutionProvider for inference")
+
+    input_names = [inp.name for inp in session.get_inputs()]
+    output_names = [out.name for out in session.get_outputs()]
+
+    LOGGER.info(f"Input names: {input_names}")
+    LOGGER.info(f"Output names: {output_names}")
+
+    inputs = {
+        "image": image.astype(np.float32),
+        "disparity_factor": np.array([disparity_factor], dtype=np.float32)
+    }
+
+    LOGGER.info("Running inference...")
+    raw_outputs = session.run(None, inputs)
+
+    outputs = {}
+
+    if len(raw_outputs) == 1:
+        concat = raw_outputs[0]
+        sizes = [3, 3, 4, 3, 1]
+        names = [
+            "mean_vectors_3d_positions",
+            "singular_values_scales",
+            "quaternions_rotations",
+            "colors_rgb_linear",
+            "opacities_alpha_channel"
+        ]
+        start = 0
+        for name, size in zip(names, sizes):
+            outputs[name] = concat[:, :, start:start + size]
+            start += size
+    elif len(raw_outputs) == 5:
+        names = [
+            "mean_vectors_3d_positions",
+            "singular_values_scales",
+            "quaternions_rotations",
+            "colors_rgb_linear",
+            "opacities_alpha_channel"
+        ]
+        for name, out in zip(names, raw_outputs):
+            outputs[name] = out
+    else:
+        for name, out in zip(output_names, raw_outputs):
+            outputs[name] = out
+
+    for name, arr in outputs.items():
+        LOGGER.info(f"  {name}: shape {arr.shape}")
+
+    return outputs
+
+
+def export_ply(outputs: dict[str, np.ndarray], output_path: str | Path,
+               focal_length_px: float, image_shape: tuple[int, int],
+               decimation: float = 1.0, depth_scale: float = 1.0) -> None:
+    """Export Gaussians to PLY file format."""
+    output_path = Path(output_path)
+
+    mean_vectors = outputs["mean_vectors_3d_positions"]
+    singular_values = outputs["singular_values_scales"]
+    quaternions = outputs["quaternions_rotations"]
+    colors = outputs["colors_rgb_linear"]
+    opacities = outputs["opacities_alpha_channel"]
+
+    mean_vectors = mean_vectors[0]
+    singular_values = singular_values[0]
+    quaternions = quaternions[0]
+    colors = colors[0]
+    opacities = opacities[0]
+
+    num_gaussians = mean_vectors.shape[0]
+    LOGGER.info(f"Exporting {num_gaussians} Gaussians to PLY")
+
+    if decimation < 1.0:
+        log_scales = np.log(np.maximum(singular_values, 1e-10))
+        scale_product = np.exp(np.sum(log_scales, axis=1))
+        importance = scale_product * opacities
+
+        indices = np.argsort(-importance)
+        keep_count = max(1, int(num_gaussians * decimation))
+        keep_indices = indices[:keep_count]
+        keep_indices.sort()
+
+        LOGGER.info(f"Decimating: keeping {keep_count} of {num_gaussians} ({decimation * 100:.1f}%)")
+
+        mean_vectors = mean_vectors[keep_indices]
+        singular_values = singular_values[keep_indices]
+        quaternions = quaternions[keep_indices]
+        colors = colors[keep_indices]
+        opacities = opacities[keep_indices]
+        num_gaussians = keep_count
+
+    vertex_data = np.zeros(num_gaussians, dtype=[
+        ('x', 'f4'), ('y', 'f4'), ('z', 'f4'),
+        ('f_dc_0', 'f4'), ('f_dc_1', 'f4'), ('f_dc_2', 'f4'),
+        ('opacity', 'f4'),
+        ('scale_0', 'f4'), ('scale_1', 'f4'), ('scale_2', 'f4'),
+        ('rot_0', 'f4'), ('rot_1', 'f4'), ('rot_2', 'f4'), ('rot_3', 'f4')
+    ])
+
+    # Model outputs [z*x_ndc, z*y_ndc, z] where z is normalized depth and x_ndc, y_ndc ∈ [-1, 1]
+    # The model's depth is scale-invariant and normalized to a small range (typically ~0.5-0.7)
+    # We need to:
+    # 1. Expand the depth range for proper 3D relief
+    # 2. Convert projective coords to camera space: x_cam = (z*x_ndc) / focal_ndc
+    
+    img_h, img_w = image_shape
+    z_raw = mean_vectors[:, 2]
+    
+    # Normalize depth to start at 1.0 and scale for better 3D relief
+    # depth_scale > 1.0 exaggerates depth differences (useful for flat scenes)
+    z_min = np.min(z_raw)
+    z_normalized = z_raw / z_min  # Now min depth = 1.0
+    
+    # Apply depth scale to exaggerate depth differences around the median
+    if depth_scale != 1.0:
+        z_median = np.median(z_normalized)
+        z_normalized = z_median + (z_normalized - z_median) * depth_scale
+    
+    # Scale factor to convert from NDC to camera space
+    # For a camera with focal length f and image width w: focal_ndc = 2*f/w
+    # With f = w (90° FOV assumption): focal_ndc = 2.0
+    focal_ndc = 2.0 * focal_length_px / img_w
+    
+    # Compute camera-space coordinates
+    # The projective values need to be scaled by the same depth normalization
+    scale_factor = 1.0 / (z_min * focal_ndc)
+    
+    vertex_data['x'] = mean_vectors[:, 0] * scale_factor
+    vertex_data['y'] = mean_vectors[:, 1] * scale_factor
+    vertex_data['z'] = z_normalized
+    
+    LOGGER.info(f"Depth range: {z_raw.min():.3f} - {z_raw.max():.3f} -> normalized: 1.0 - {z_normalized.max():.3f}")
+
+    for i in range(num_gaussians):
+        r, g, b = colors[i]
+        srgb_r = linear_to_srgb(float(r))
+        srgb_g = linear_to_srgb(float(g))
+        srgb_b = linear_to_srgb(float(b))
+
+        vertex_data['f_dc_0'][i] = rgb_to_sh(srgb_r)
+        vertex_data['f_dc_1'][i] = rgb_to_sh(srgb_g)
+        vertex_data['f_dc_2'][i] = rgb_to_sh(srgb_b)
+
+    vertex_data['opacity'] = inverse_sigmoid(opacities)
+
+    # Scale the Gaussian sizes to match the transformed coordinate space
+    vertex_data['scale_0'] = np.log(np.maximum(singular_values[:, 0] * scale_factor, 1e-10))
+    vertex_data['scale_1'] = np.log(np.maximum(singular_values[:, 1] * scale_factor, 1e-10))
+    vertex_data['scale_2'] = np.log(np.maximum(singular_values[:, 2] / z_min, 1e-10))  # Z scale uses depth normalization
+
+    vertex_data['rot_0'] = quaternions[:, 0]
+    vertex_data['rot_1'] = quaternions[:, 1]
+    vertex_data['rot_2'] = quaternions[:, 2]
+    vertex_data['rot_3'] = quaternions[:, 3]
+
+    vertex_element = PlyElement.describe(vertex_data, 'vertex')
+
+    # Extrinsic: 4x4 identity matrix as 16 separate properties
+    extrinsic_data = np.zeros(1, dtype=[('extrinsic', 'f4', (16,))])
+    extrinsic_data['extrinsic'][0] = [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]
+    extrinsic_element = PlyElement.describe(extrinsic_data, 'extrinsic')
+
+    img_h, img_w = image_shape
+    # Intrinsic: 3x3 matrix as 9 separate properties
+    intrinsic_data = np.zeros(1, dtype=[('intrinsic', 'f4', (9,))])
+    intrinsic_data['intrinsic'][0] = [focal_length_px, 0, img_w / 2, 0, focal_length_px, img_h / 2, 0, 0, 1]
+    intrinsic_element = PlyElement.describe(intrinsic_data, 'intrinsic')
+
+    # Image size: 2 separate uint32 properties
+    image_size_data = np.zeros(1, dtype=[('image_size', 'u4', (2,))])
+    image_size_data['image_size'][0] = [img_w, img_h]
+    image_size_element = PlyElement.describe(image_size_data, 'image_size')
+
+    # Frame: 2 separate int32 properties
+    frame_data = np.zeros(1, dtype=[('frame', 'i4', (2,))])
+    frame_data['frame'][0] = [1, num_gaussians]
+    frame_element = PlyElement.describe(frame_data, 'frame')
+
+    z_values = mean_vectors[:, 2]
+    z_safe = np.maximum(z_values, 1e-6)
+    disparities = 1.0 / z_safe
+    disparities.sort()
+    disparity_10 = disparities[int(len(disparities) * 0.1)] if len(disparities) > 0 else 0.0
+    disparity_90 = disparities[int(len(disparities) * 0.9)] if len(disparities) > 0 else 1.0
+    disparity_data = np.zeros(1, dtype=[('disparity', 'f4', (2,))])
+    disparity_data['disparity'][0] = [disparity_10, disparity_90]
+    disparity_element = PlyElement.describe(disparity_data, 'disparity')
+
+    # Color space: single uchar property
+    color_space_data = np.zeros(1, dtype=[('color_space', 'u1')])
+    color_space_data['color_space'][0] = 1
+    color_space_element = PlyElement.describe(color_space_data, 'color_space')
+
+    # Version: 3 uchar properties
+    version_data = np.zeros(1, dtype=[('version', 'u1', (3,))])
+    version_data['version'][0] = [1, 5, 0]
+    version_element = PlyElement.describe(version_data, 'version')
+
+    PlyData([
+        vertex_element,
+        extrinsic_element,
+        intrinsic_element,
+        image_size_element,
+        frame_element,
+        disparity_element,
+        color_space_element,
+        version_element
+    ], text=False).write(str(output_path))
+
+    LOGGER.info(f"Saved PLY with {num_gaussians} Gaussians to {output_path}")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="ONNX Inference for SHARP - Generate 3D Gaussians from an image"
+    )
+    parser.add_argument("-m", "--model", type=str, required=True,
+                        help="Path to ONNX model file")
+    parser.add_argument("-i", "--input", type=str, required=True,
+                        help="Path to input image")
+    parser.add_argument("-o", "--output", type=str, required=True,
+                        help="Path to output file (.ply)")
+    parser.add_argument("-d", "--decimate", type=float, default=1.0,
+                        help="Decimation ratio 0.0-1.0 (default: 1.0 = keep all)")
+    parser.add_argument("--disparity-factor", type=float, default=1.0,
+                        help="Disparity factor for depth conversion (default: 1.0)")
+    parser.add_argument("--depth-scale", type=float, default=1.0,
+                        help="Depth exaggeration factor (>1.0 increases 3D relief, default: 1.0)")
+
+    args = parser.parse_args()
+
+    # Preprocess image
+    image, focal_length_px, image_shape = preprocess_image(args.input)
+
+    # Run inference
+    outputs = run_inference(args.model, image, args.disparity_factor)
+
+    # Export to PLY
+    export_ply(outputs, args.output, focal_length_px, image_shape, args.decimate, args.depth_scale)
+
+
+if __name__ == "__main__":
+    main()

sharp_fp16.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a45328f6c1b7f7d57d3a19c1d1b71647bff72a42a532814e826cf86d1e8a4040
+size 1319661752