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README.md CHANGED
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  ---
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  license: cc-by-4.0
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
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  license: cc-by-4.0
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  ---
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+
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+ # Neural 3D Video Dataset - Processed
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+
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+ This directory contains preprocessed multi-view video data from the Neural 3D Video dataset, converted into a format suitable for 4D reconstruction and novel view synthesis tasks.
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+
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+ ## Dataset Overview
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+
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+ **Source Dataset**: Neural 3D Video
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+ **License**: CC-BY-4.0
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+ **Processed Scenes**: 5 dynamic cooking scenes captured from multiple camera angles
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+
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+ ### Scenes
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+
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+ | Scene | Description | Cameras | Frames |
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+ |-------|-------------|---------|--------|
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+ | `coffee_martini` | Making a coffee martini cocktail | 18 | 32 |
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+ | `cook_spinach` | Cooking spinach in a pan | 18 | 32 |
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+ | `cut_roasted_beef` | Cutting roasted beef | 18 | 32 |
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+ | `flame_salmon_1` | Flambé salmon preparation | 18 | 32 |
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+ | `sear_steak` | Searing steak in a pan | 18 | 32 |
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+
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+ ## Directory Structure
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+
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+ ```
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+ Neural-3D-Video-Dataset/
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+ ├── README.md (this file)
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+ ├── coffee_martini_processed/
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+ │ ├── 256/ # 256×256 resolution
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+ │ │ ├── images/ # 32 frame images
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+ │ │ │ ├── sample_000_cam00.jpg
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+ │ │ │ ├── sample_001_cam01.jpg
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+ │ │ │ └── ...
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+ │ │ ├── transforms.json # Camera poses (JSON format)
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+ │ │ ├── transforms.npz # Camera poses (NumPy format)
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+ │ │ └── camera_visualization.html # Interactive 3D camera viewer
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+ │ └── 512/ # 512×512 resolution
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+ │ ├── images/
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+ │ ├── transforms.json
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+ │ ├── transforms.npz
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+ │ └── camera_visualization.html
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+ ├── cook_spinach_processed/
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+ │ ├── 256/ ...
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+ │ └── 512/ ...
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+ ├── cut_roasted_beef_processed/
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+ │ ├── 256/ ...
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+ │ └── 512/ ...
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+ ├── flame_salmon_1_processed/
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+ │ ├── 256/ ...
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+ │ └── 512/ ...
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+ └── sear_steak_processed/
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+ ├── 256/ ...
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+ └── 512/ ...
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+ ```
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+
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+ ## Data Format
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+
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+ ### Camera Poses (`transforms.json`)
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+
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+ The camera poses are stored in a JSON file with the following structure:
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+
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+ ```json
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+ {
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+ "frames": [
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+ {
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+ "front": {
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+ "timestamp": 0,
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+ "file_path": "./images/sample_000_cam00.jpg",
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+ "w": 256,
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+ "h": 256,
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+ "fx": 341.33,
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+ "fy": 341.33,
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+ "cx": 128.0,
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+ "cy": 128.0,
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+ "w2c": [[...], [...], [...], [...]], // 4×4 world-to-camera matrix
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+ "c2w": [[...], [...], [...]], // 3×4 camera-to-world matrix
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+ "blender_camera_location": [x, y, z] // Camera position in world coordinates
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+ }
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+ },
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+ ...
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+ ]
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+ }
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+ ```
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+
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+ **Intrinsics** (camera internal parameters):
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+ - **256×256**: `fx = fy = 341.33`, `cx = cy = 128.0`
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+ - **512×512**: `fx = fy = 682.67`, `cx = cy = 256.0`
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+
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+ **Extrinsics** (camera external parameters):
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+ - `w2c`: 4×4 world-to-camera transformation matrix
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+ - `c2w`: 3×4 camera-to-world transformation matrix (rotation + translation)
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+ - `blender_camera_location`: 3D camera position `[x, y, z]` in world coordinates
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+
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+ ### NumPy Format (`transforms.npz`)
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+
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+ For convenience, camera parameters are also provided in NumPy format:
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+
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+ ```python
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+ import numpy as np
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+
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+ data = np.load('transforms.npz')
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+ intrinsics = data['intrinsics'] # (32, 3, 3) - intrinsic matrices
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+ extrinsics_w2c = data['extrinsics_w2c'] # (32, 4, 4) - world-to-camera
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+ extrinsics_c2w = data['extrinsics_c2w'] # (32, 4, 4) - camera-to-world
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+ camera_positions = data['camera_positions'] # (32, 3) - camera locations
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+ ```
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+
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+ ### Frame Images
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+
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+ - **Format**: JPEG
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+ - **Resolutions**: 256×256 and 512×512
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+ - **Count**: 32 frames per scene
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+ - **Naming**: `sample_{frame:03d}_cam{camera:02d}.jpg`
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+
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+ Each frame is extracted from a different camera view:
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+ - Frame 0 → cam00
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+ - Frame 1 → cam01
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+ - ...
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+ - Frame 17 → cam20
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+ - Frame 18 → cam00 (loops back)
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+ - ...
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+ - Frame 31 → cam14
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+
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+ ## Data Processing
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+
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+ ### Original Data
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+
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+ - **Source resolution**: 2704×2028 (4:3 aspect ratio)
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+ - **Original format**: Multi-view MP4 videos
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+ - **Camera model**: LLFF format with `poses_bounds.npy`
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+
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+ ### Processing Pipeline
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+
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+ 1. **Center Crop**: 2704×2028 → 2028×2028 (square)
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+ 2. **Resize**: 2028×2028 → 256×256 or 512×512
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+ 3. **Intrinsics Adjustment**: Focal length and principal point adjusted for crop and resize
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+ 4. **Extrinsics Extraction**: Camera poses extracted from LLFF format
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+ 5. **Format Conversion**: Converted to standard c2w/w2c matrices
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+
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+ ### Frame Sampling Strategy
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+
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+ To capture the dynamic motion from multiple viewpoints, frames are sampled such that each frame shows the scene from a different camera angle in sequence. This creates a "synchronized" multi-view video where:
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+ - The temporal progression shows the dynamic action
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+ - Each frame provides a different spatial viewpoint
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+ - Camera angles loop after exhausting all 18 cameras
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+
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+ ## Camera Visualization
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+
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+ Each processed scene includes an interactive 3D camera visualization (`camera_visualization.html`):
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+
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+ - **View camera positions** and orientations in 3D space
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+ - **Interactive**: Rotate, pan, and zoom to explore the camera rig
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+ - **Camera frustums**: Visualize the viewing direction and field of view
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+ - **Trajectory path**: See the sequence of frames and camera transitions
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+ - **Powered by Plotly**: High-quality interactive graphics
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+
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+ Open the HTML file in any web browser to explore the camera setup.
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+
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+ ## Usage Examples
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+
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+ ### Loading Camera Poses (Python)
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+
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+ ```python
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+ import json
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+ import numpy as np
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+
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+ # Load from JSON
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+ with open('coffee_martini_processed/256/transforms.json', 'r') as f:
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+ data = json.load(f)
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+
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+ # Access first frame
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+ frame0 = data['frames'][0]['front']
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+ print(f"Camera intrinsics: fx={frame0['fx']}, fy={frame0['fy']}")
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+ print(f"Camera position: {frame0['blender_camera_location']}")
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+ print(f"Image path: {frame0['file_path']}")
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+
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+ # Load from NumPy
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+ poses = np.load('coffee_martini_processed/256/transforms.npz')
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+ intrinsics = poses['intrinsics'] # (32, 3, 3)
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+ c2w = poses['extrinsics_c2w'] # (32, 4, 4)
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+ ```
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+
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+ ### Loading Images
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+
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+ ```python
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+ import cv2
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+ import os
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+
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+ scene_dir = 'coffee_martini_processed/256'
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+ img_dir = os.path.join(scene_dir, 'images')
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+
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+ # Load all frames
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+ frames = []
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+ for i in range(32):
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+ img_path = os.path.join(img_dir, f'sample_{i:03d}_cam*.jpg')
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+ # Find the actual file (camera number may vary)
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+ import glob
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+ img_file = glob.glob(img_path)[0]
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+ img = cv2.imread(img_file)
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+ frames.append(img)
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+
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+ print(f"Loaded {len(frames)} frames, shape: {frames[0].shape}")
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+ ```
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+
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+ ### PyTorch Dataset Example
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+
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+ ```python
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+ import torch
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+ from torch.utils.data import Dataset
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+ from PIL import Image
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+ import json
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+ import numpy as np
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+
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+ class Neural3DVideoDataset(Dataset):
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+ def __init__(self, scene_dir):
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+ self.scene_dir = scene_dir
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+
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+ # Load transforms
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+ with open(os.path.join(scene_dir, 'transforms.json'), 'r') as f:
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+ self.data = json.load(f)
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+
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+ self.frames = self.data['frames']
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+
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+ def __len__(self):
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+ return len(self.frames)
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+
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+ def __getitem__(self, idx):
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+ frame_data = self.frames[idx]['front']
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+
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+ # Load image
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+ img_path = os.path.join(self.scene_dir, frame_data['file_path'])
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+ img = Image.open(img_path).convert('RGB')
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+ img = torch.from_numpy(np.array(img)).float() / 255.0
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+
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+ # Get camera parameters
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+ intrinsics = torch.tensor([
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+ [frame_data['fx'], 0, frame_data['cx']],
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+ [0, frame_data['fy'], frame_data['cy']],
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+ [0, 0, 1]
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+ ], dtype=torch.float32)
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+
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+ c2w = torch.tensor(frame_data['c2w'], dtype=torch.float32)
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+
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+ return {
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+ 'image': img,
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+ 'intrinsics': intrinsics,
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+ 'c2w': c2w,
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+ 'timestamp': frame_data['timestamp']
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+ }
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+
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+ # Usage
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+ dataset = Neural3DVideoDataset('coffee_martini_processed/256')
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+ sample = dataset[0]
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+ print(f"Image shape: {sample['image'].shape}")
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+ print(f"Camera position: {sample['c2w'][:, 3]}")
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+ ```
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+
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+ ## Technical Details
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+
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+ ### Camera Configuration
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+
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+ - **Number of cameras**: 18 per scene
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+ - **Camera arrangement**: Surrounding the scene in a roughly circular pattern
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+ - **Frame rate**: 30 FPS (original videos)
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+ - **Camera model**: Pinhole camera with radial distortion (pre-undistorted)
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+
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+ ### Coordinate System
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+
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+ - **World coordinates**: Right-handed coordinate system
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+ - **Camera coordinates**:
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+ - X-axis: Right
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+ - Y-axis: Down
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+ - Z-axis: Forward (viewing direction)
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+ - **c2w matrix**: Transforms from camera space to world space
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+ - **w2c matrix**: Transforms from world space to camera space
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+
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+ ### Quality Settings
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+
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+ - **JPEG quality**: 95
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+ - **Interpolation**: Bilinear (cv2.INTER_LINEAR)
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+ - **Color space**: RGB (8-bit per channel)
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+
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+ ## Citation
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+
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+ If you use this dataset in your research, please cite the original Neural 3D Video dataset:
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+
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+ ```bibtex
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+ @article{neural3dvideo2021,
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+ title={Neural 3D Video Synthesis},
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+ author={Author Names},
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+ journal={Conference/Journal Name},
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+ year={2021}
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+ }
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+ ```
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+
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+ ## Processing Scripts
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+
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+ The data was processed using custom scripts available in the parent directory:
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+ - `create_sync_video_with_poses.py` - Single scene processing
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+ - `batch_process_scenes.py` - Batch processing for all scenes
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+
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+ ## License
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+
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+ This processed dataset inherits the CC-BY-4.0 license from the original Neural 3D Video dataset. Please respect the license terms when using this data.
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+
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+ ## Contact
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+
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+ For questions or issues regarding this processed dataset, please contact the dataset maintainer.
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