WildDet3D-Stereo4D-Bench

# WildDet3D Stereo4D Benchmark 3D object detection benchmark with ground-truth stereo depth from Stereo4D. Images extracted from Stereo4D videos (1 frame per video from the test set, representing 7% of all Stereo4D videos). | Split | Images | Annotations | |-------|--------|-------------| | Val | 383 | 2,782 | **Note:** The test set is held out for hidden evaluation and is not publicly available. ## Download ```bash pip install huggingface_hub # Download everything huggingface-cli download weikaih/WildDet3D-Stereo4D-Bench --repo-type dataset --local-dir WildDet3D-Stereo4D-Bench ``` After downloading, extract the packed archives: ```bash cd WildDet3D-Stereo4D-Bench tar xzf packed/images.tar.gz tar xzf packed/depth.tar.gz tar xzf packed/camera_parameters.tar.gz ``` ## Directory Structure ``` WildDet3D-Stereo4D-Bench/ ├── annotations/ │ ├── Stereo4D_val.json │ ├── Stereo4D_test.json │ └── Stereo4D_val_class_map.json ├── images/ # 7,704 images (512x512) ├── depth/ # 7,704 .npy depth maps (float32, meters) └── camera_parameters/ # 7,704 .json camera intrinsics ``` ## Annotation Format (COCO3D) Same format as [WildDet3D-Data](https://huggingface.co/datasets/weikaih/WildDet3D-Data). Each annotation JSON contains: - **`images`**: image metadata with `file_path`, `K` (intrinsics), `width`, `height` - **`annotations`**: 3D bounding boxes with `center_cam`, `dimensions` [W,H,L], `R_cam`, `bbox3D_cam`, `bbox2D_proj`, `valid3D` - **`categories`**: category list ## Depth Format Each `.npy` file is a float32 2D array at image resolution (512x512). **Values are in meters.** ## License CC BY-NC This dataset is licensed under CC BY-NC. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.