Datasets:
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 — 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:
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}.jsonunderinfo.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)
{
"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 releasedframe_NNNNNN.jpgnames).
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
@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}
}