| | ---
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| | license: cc-by-4.0
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| | ---
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| |
|
| | # Neural 3D Video Dataset - Processed
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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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| |
|
| | ### 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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| |
|
| | ## 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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| |
|
| | ## 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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| |
|
| | ### 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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| |
|
| | ## 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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| |
|
| | ### 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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| |
|
| | ### 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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| |
|
| | ## Technical Details
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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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| |
|
| | ## 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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| |
|
| | ## 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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| |
|
| | ## 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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| |
|
| | ## Contact
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| |
|
| | For questions or issues regarding this processed dataset, please contact the dataset maintainer. |
