README.md

metadata

license: cc-by-4.0
task_categories:
  - object-detection
tags:
  - 3d-object-detection
  - 3d-bounding-box
  - monocular-3d
  - in-the-wild
  - benchmark
pretty_name: WildDet3D Benchmark
size_categories:
  - 1K<n<10K

WildDet3D Benchmark

In-the-wild 3D object detection benchmark (val and test splits) from COCO, LVIS, and Objects365.

Split	Images	Annotations	Categories
Val	2,470	9,256	785

Note: The test set is held out for hidden evaluation and is not publicly available. Please submit predictions to [TODO: evaluation server] for test set evaluation.

Download

pip install huggingface_hub

# Download everything
huggingface-cli download allenai/WildDet3D-Bench --repo-type dataset --local-dir WildDet3D-Bench

After downloading, extract depth and camera archives:

cd WildDet3D-Bench
tar xzf packed/depth_val.tar.gz
tar xzf packed/camera_val.tar.gz

Directory Structure

WildDet3D-Bench/
├── annotations/
│   ├── InTheWild_v3_val.json
│   ├── InTheWild_v3_test.json
│   └── InTheWild_v3_val_class_map.json
├── depth/{val,test}/                    # Monocular depth maps
│   └── {source}_{formatted_id}.npz     # float32, mm (divide by 1000 for meters)
└── camera/{val,test}/                   # Camera intrinsics
    └── {source}_{formatted_id}.json    # K + image_size

Source Images

Images must be downloaded from their original sources:

Source	Directory	Download
COCO val2017	images/coco_val/	https://cocodataset.org/
COCO train2017	images/coco_train/	https://cocodataset.org/
Objects365 val	images/obj365_val/	https://www.objects365.org/

Annotation Format (COCO3D)

Same format as WildDet3D-Data. Key fields:

• valid3D: true = valid 3D annotation, false = 3D box filtered out (2D box still valid)
• center_cam: 3D box center in camera coordinates (meters)
• dimensions: [width, height, length] in meters (Omni3D convention)
• R_cam: 3x3 rotation matrix (gravity-aligned, local Y = up)
• bbox3D_cam: 8 corner points of the 3D bounding box
• bbox2D_proj: 2D bounding box [x1, y1, x2, y2]

License

CC BY 4.0