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0000/annotations
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0000/depths/depth_00000
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hf://datasets/ZhengGuangze/Kubric_vlbm@2ac37d60214d77093c4c246f9f51e548fdbe8691/0000.tar.gz
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Kubric CoTracker3 Dataset
This dataset contains 3D point tracking sequences generated from Kubric synthetic scenes, converted to the Flock4D format. The dataset includes RGB images, depth maps, 2D/3D trajectories, camera parameters, and scene metadata for each sequence.
Dataset Description
This dataset contains point tracking sequences with the following characteristics:
- Total Sequences: 5,869 sequences
- Format: Flock4D-compatible format
- Source: Generated from Kubric synthetic scenes using CoTracker3
- Resolution: 512Γ512 pixels
- License: Please refer to the original Kubric and CoTracker3 licenses
Dataset Structure
Each sequence directory contains:
{sequence_id}/
βββ rgbs/
β βββ rgb_00000.jpg
β βββ rgb_00001.jpg
β βββ ...
βββ depths/
β βββ depth_00000.npz
β βββ depth_00001.npz
β βββ ...
βββ annotations.npz
βββ scene_info.json
File Descriptions
- rgbs/: RGB images in JPEG format (512Γ512 pixels)
- depths/: Depth maps in npz format (512Γ512 pixels). The depth values originate from Kubric's metric depth output (in meters).
- annotations.npz: NumPy compressed file containing:
trajs_2d: 2D trajectories(T, N, 2)- T frames, N points, (x, y) coordinates (float16)trajs_3d: 3D trajectories(T, N, 3)- T frames, N points, (x, y, z) coordinates in camera space (float16)visibilities: Visibility flags(T, N)- 1.0 for visible, 0.0 for invisible (float16)intrinsics: Camera intrinsic matrices(T, 3, 3)- one per frame (float16)extrinsics: Camera extrinsic matrices(T, 4, 4)- world-to-camera transformation (float16)
- scene_info.json: Scene metadata including sensor parameters
Data Specifications
- Image Resolution: 512 Γ 512 pixels
- Number of Points: 32,768 points per sequence
- Number of Frames: 120 frames per sequence
- Data Types:
- Images: JPEG (RGB) and PNG (depth, uint16)
- Annotations: float16 for efficient storage
- Depth Source: The depth maps originate from Kubric's metric depth output (meters).
- Coordinate Systems:
- 2D trajectories: Image pixel coordinates (x, y)
- 3D trajectories: Camera coordinate system (x, y, z)
- Extrinsics: World-to-camera transformation matrices (4Γ4)
Usage
Loading Data in Python
import numpy as np
from PIL import Image
from pathlib import Path
import json
# Load a sequence
seq_dir = Path("data/kubric_cotracker3/0000")
# Load annotations
annotations = np.load(seq_dir / "annotations.npz", allow_pickle=True)
trajs_2d = annotations['trajs_2d'] # (T, N, 2)
trajs_3d = annotations['trajs_3d'] # (T, N, 3)
visibilities = annotations['visibilities'] # (T, N)
intrinsics = annotations['intrinsics'] # (T, 3, 3)
extrinsics = annotations['extrinsics'] # (T, 4, 4)
# Load images
frame_idx = 0
rgb_img = Image.open(seq_dir / "rgbs" / f"rgb_{frame_idx:05d}.jpg")
depth_img = Image.open(seq_dir / "depths" / f"depth_{frame_idx:05d}.png")
# Load scene info
with open(seq_dir / "scene_info.json", 'r') as f:
scene_info = json.load(f)
Converting 3D Points to World Coordinates
# Transform 3D points from camera to world coordinates
extrinsics_inv = np.linalg.inv(extrinsics[frame_idx])
R_inv = extrinsics_inv[:3, :3]
t_inv = extrinsics_inv[:3, 3]
points_3d_camera = trajs_3d[frame_idx] # (N, 3) in camera coordinates
points_3d_world = (R_inv @ points_3d_camera.T).T + t_inv # (N, 3) in world coordinates
Dataset Statistics
- Total Sequences: 5,869
- Frames per Sequence: 120 frames
- Points per Sequence: 32,768 points
- Image Format: RGB JPEG (512Γ512)
- Depth Format: NPZ (512Γ512)
- Total Size: ~313 GB (uncompressed)
Dataset Details
Annotations Format
The annotations.npz file contains the following arrays:
trajs_2d(T, N, 2): 2D pixel coordinates for each point across all frames- T: number of frames (120)
- N: number of points (32,768)
- Coordinates: (x, y) in pixel space
trajs_3d(T, N, 3): 3D coordinates in camera space- Coordinates: (x, y, z) in camera coordinate system
visibilities(T, N): Visibility flags- 1.0: point is visible in the frame
- 0.0: point is occluded or outside the frame
intrinsics(T, 3, 3): Camera intrinsic matrices (one per frame)- Format: standard camera intrinsic matrix
- Used for 2D to 3D conversion
extrinsics(T, 4, 4): Camera extrinsic matrices (world-to-camera transformation)- Format: 4Γ4 transformation matrix
- Used for coordinate system transformation
Scene Information
The scene_info.json file contains metadata about each scene:
{
"sensor_width": 32.0,
"sensor_height": 32.0,
"focal_length": <focal_length>,
"assets": [],
"character": []
}
Citation
If you use this dataset, please cite:
- The original Kubric paper
- CoTracker3 paper
- Flock4D dataset (if applicable)
Contact
For questions or issues regarding this dataset, please open an issue in the repository.
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