| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - monocular |
| - 3d |
| - wildlife |
| - drone |
| task_categories: |
| - object-detection |
| pretty_name: WildBox |
| --- |
| |
| # WildBox |
|
|
| A 6-class monocular 3D wildlife detection dataset with KITTI / Omni3D-compatible |
| labels, derived from drone footage of African megafauna. This README describes |
| the contents of this release and the conventions used. |
|
|
| Release v1.1: annotations are exported from the human-corrected annotation tracks, |
| and the KITTI label channel is written in a per-frame upright rectified reference |
| frame with standard calibration files (see `kitti_labels` section below). |
|
|
| Paper: [WildBox: A Dataset and Benchmark for Aerial Monocular 3D Detection of |
| African Savanna Wildlife](https://arxiv.org/abs/2606.21309) — NeurIPS 2026, |
| Evaluations and Datasets Track. |
|
|
| --- |
|
|
| ## Contents of this release |
|
|
| ``` |
| README.md ← this file |
| WildBox_train.json ← Omni3D-format train split |
| WildBox_val.json ← Omni3D-format val split |
| metadata/ ← per-segment / per-video / per-track statistics (CSV) |
| checkpoints/ ← paper baseline checkpoints (see checkpoints/CHECKPOINTS_README.md) |
| <group>/ ← 11 group folders (see table below) |
| └── <video>.zip ← 64 per-video archives total |
| ``` |
|
|
| Each per-video archive expands to `<video>/seg{N}/` with frames + cameras + |
| KITTI labels + calibration + tracking metadata (see "Per-segment contents" below). |
|
|
| To use the dataset, place all of the above into a single working directory |
| and extract every `<video>.zip` *in place* (i.e. inside its group folder) |
| so that the `file_path` fields in the two JSONs resolve: |
|
|
| ```bash |
| for z in */*.zip; do unzip -d "$(dirname "$z")" "$z"; done |
| ``` |
|
|
| Each `file_path` in the JSONs is relative to that working directory, e.g. |
| `gira1/DJI_20240118140338_0007_V/seg1/frame_000001.jpg`. |
|
|
| --- |
|
|
| ## Headline statistics |
|
|
| | Split | Images | Annotations | Categories | |
| |-------|-------:|------------:|-----------:| |
| | train | 45 979 | 170 554 | 6 | |
| | val | 13 779 | 66 951 | 6 | |
|
|
| The 6 categories are: `giraffe`, `grevys_zebra`, `elephant`, `plains_zebra`, |
| `rhino`, `gazelle` (canonical IDs 1000 – 1005). |
|
|
| Each group folder contains annotations of a single species: |
|
|
| | Group | Species (category) | |
| |-------|---| |
| | gira1, gira2 | giraffe | |
| | zebr1, zebr3 | plains_zebra | |
| | zebr2 | grevys_zebra | |
| | elep1, elep2, elep3 | elephant | |
| | rhin1, rhin2 | rhino | |
| | gaze1 | gazelle | |
|
|
| Frames are 1920 × 1080 RGB JPEGs. |
|
|
| --- |
|
|
| ## Per-segment contents (inside each `<video>.zip`) |
|
|
| ``` |
| <video>/ |
| └── seg{N}/ |
| ├── frame_NNNNNN.jpg ← RGB 1920×1080 |
| ├── cameras.json ← per-frame intrinsics K + extrinsic [R|t] |
| ├── kitti_labels/ |
| │ └── frame_NNNNNN.txt ← KITTI 3D-detection format (rectified frame) |
| ├── calib/ |
| │ └── frame_NNNNNN.txt ← per-frame KITTI calibration (P2, R0_rect) |
| └── tracking_summary.json ← annotator-native (full 3×3 R, centroid loc) |
| ``` |
|
|
| --- |
|
|
| ## kitti_labels/*.txt — KITTI 3D detection format (rectified frame) |
| |
| One line per object, space-separated, 16 fields: |
| |
| ``` |
| class trunc occ alpha x1 y1 x2 y2 h w l X Y_bot Z ry score |
| ``` |
| |
| | Field | Meaning | |
| |---------------|---------| |
| | class | species name (e.g. `giraffe`, `plains_zebra`) | |
| | trunc | truncation [0, 1] | |
| | occ | occlusion {0, 1, 2, 3} | |
| | alpha | observation angle, radians | |
| | x1 y1 x2 y2 | 2D bbox in image pixels at 1920 × 1080 | |
| | h w l | cuboid extents along rectified Y / Z / X axes | |
| | X Y_bot Z | **bottom-center** of cuboid in RECTIFIED camera coords (Y points down) | |
| | ry | yaw rotation around the rectified Y axis (radians) | |
| | score | 1.0 for ground-truth | |
| |
| **The 3D fields live in a per-frame upright rectified camera frame**, exactly as |
| in original KITTI (where labels live in the `R0_rect` frame). The rectified |
| frame is chosen per frame so that its −Y axis aligns with the animals' up |
| direction; standing animals are therefore yaw-only and the single `ry` angle |
| represents the full annotated orientation without loss. To project a 3D point |
| `X_rect` to pixels, use the per-frame calibration: |
| |
| ``` |
| x_img ~ P2 · [X_rect; 1] (P2 from calib/frame_NNNNNN.txt) |
| ``` |
| |
| Standard KITTI tooling that projects boxes through `P2` renders these labels |
| correctly. Do NOT project rectified coordinates with the raw camera K from |
| `cameras.json` — use `P2`, or rotate back to the camera frame first |
| (`X_cam = R_cam_from_rect · X_rect`, where `P2 = [K · R_cam_from_rect | 0]`). |
| |
| Dimensions order here is KITTI-standard `(h, w, l)`; the Omni3D JSONs and |
| `tracking_summary.json` use the annotator-native order (see below). |
|
|
| --- |
|
|
| ## Coordinate conventions |
|
|
| * Image coordinates: 1920 × 1080, origin top-left, X right, Y down. |
| * Camera frame: X right, Y down, Z forward (OpenCV / Omni3D / KITTI). |
| * Per-segment scale normalisation: every 3D coordinate is divided by the |
| median |z_cam| of valid GT cuboids in that segment, so median |z_cam| ≈ 1 |
| per segment. Per-segment scale factors are listed in |
| `WildBox_{train,val}.json` under `info.scene_scales`. The KITTI labels and |
| the Omni3D JSONs share this same per-segment gauge. |
|
|
| --- |
|
|
| ## kitti_labels vs. Omni3D JSON / tracking_summary.json |
|
|
| | Property | kitti_labels | tracking_summary.json / Omni3D JSON | |
| |------------------|-------------------------------------|-------------------------------------| |
| | Reference frame | per-frame upright rectified (calib) | camera frame | |
| | Location | bottom-center of cuboid | centroid of cuboid | |
| | Dimensions order | (h, w, l) — KITTI standard | (l, w, h) — annotator native | |
| | Rotation | yaw `ry` in the rectified frame | full 3 × 3 matrix | |
|
|
| Both channels are exported from the same human-corrected annotation tracks and |
| agree exactly; use whichever format your pipeline expects. |
|
|
| --- |
|
|
| ## cameras.json (per segment) |
|
|
| ```json |
| { |
| "cameras": [ |
| { |
| "image_name": "frame_000001.jpg", |
| "image_width": 1920, |
| "image_height": 1080, |
| "intrinsic": [[fx, 0, cx], [0, fy, cy], [0, 0, 1]], |
| "extrinsic": [[r11, r12, r13, tx], ...] // 3 × 4 |
| }, |
| ... |
| ] |
| } |
| ``` |
|
|
| Intrinsics K are calibrated for 1920 × 1080. Extrinsics map world → camera |
| coords (`X_cam = R · X_world + t`), with the segment's first frame as the |
| world origin. |
|
|
| --- |
|
|
| ## Omni3D JSONs (`WildBox_{train,val}.json`) |
| |
| Top-level keys: `info`, `categories`, `images`, `annotations` — standard |
| Omni3D / COCO layout. |
| |
| `images[i]` keys: |
| * `id` (int) |
| * `dataset_id` (int) |
| * `file_path` (str, relative to this directory) |
| * `height`, `width` (int — 1920 × 1080) |
| * `K` (3 × 3, intrinsic at native resolution) |
|
|
| `annotations[i]` follows the Omni3D 3D-cuboid schema (centroid location, |
| full 3 × 3 rotation `R_cam`, full-precision dimensions `[W, H, L]`, |
| tight 2D box `bbox` in XYWH), plus `track_id` for per-animal identity |
| within a segment. `info.scene_scales` holds the per-segment scale factors |
| and `info.tracking_provenance` records the annotation source per segment. |
|
|
| --- |
|
|
| ## metadata/ — summary statistics (CSV) |
|
|
| Derived purely from the two Omni3D JSONs (no flight telemetry). All three |
| files share the keys `split,group,video[,segment]`. |
|
|
| * `metadata/segments.csv` — one row per segment (345): |
| `species, n_frames, n_annotations, n_tracks, herd_density`. |
| * `metadata/videos.csv` — one row per video (64): same columns aggregated |
| over the video's segments. |
| * `metadata/tracks.csv` — one row per track (1,478): |
| `track_id, species, n_annotations, first_frame, last_frame` |
| (frame numbers as in the released `frame_NNNNNN.jpg` names). |
|
|
| `herd_density` is the mean number of annotated animals per frame |
| (`n_annotations / n_frames`). A track is one animal instance followed |
| through one segment; `track_id` is numbered within a segment, so tracks |
| are keyed by `(video, segment, track_id)`. |
|
|
| --- |
|
|
| ## Source / provenance |
|
|
| Each segment was tracked and 3D-annotated via the WildLIFT-assisted annotator |
| pipeline and human-corrected in the annotation tool. Release v1.1 exports the |
| human-corrected tracks for both label channels. Intrinsics and 2D boxes are at |
| native frame resolution (1920 × 1080); the KITTI channel stores 3D in the |
| per-frame rectified frame described above, with calibration shipped per frame. |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{shukla2026wildbox, |
| title = {{WildBox}: A Dataset and Benchmark for Aerial Monocular {3D} Detection of {African} Savanna Wildlife}, |
| author = {Shukla, Vandita and Meier, Kilian and Laporte-Devylder, Lucie and Rondeau Saint-Jean, Camille and Kline, Jenna M. and Costelloe, Blair R. and Tuia, Devis and Remondino, Fabio and Risse, Benjamin}, |
| booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track}, |
| year = {2026}, |
| url = {https://openreview.net/forum?id=AXawAJRwDi}, |
| eprint = {2606.21309}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CV} |
| } |
| ``` |
|
|
