WildDet3D-Data: Dataset Preparation Guide

Overview

WildDet3D-Data consists of 3D bounding box annotations for in-the-wild images from COCO, LVIS, Objects365, and V3Det. The dataset is split into:

Split	Description	Annotation Source	Images	Annotations	Categories
Val	Validation set	Human	2,470	9,256	785
Test	Test set	Human	2,433	5,596	633
Train (Human)	Human-reviewed annotations only	Human	102,979	229,934	11,879
Train (Essential)	Human + VLM-qualified small objects	Human + VLM	102,979	412,711	12,064
Train (Synthetic)	VLM auto-selected annotations	VLM	896,004	3,483,292	11,896
Total			1,003,886	3,910,855	13,499

Directory Structure

After downloading and extracting, the dataset should be organized as:

WildDet3D-Data/
├── README.md
├── annotations/
│   ├── InTheWild_v3_val.json                    # Val
│   ├── InTheWild_v3_test.json                   # Test
│   ├── InTheWild_v3_train_human_only.json       # Train (Human) — COCO, LVIS, Obj365
│   ├── InTheWild_v3_train_human.json            # Train (Essential) — COCO, LVIS, Obj365
│   ├── InTheWild_v3_train_synthetic.json        # Train (Synthetic) — COCO, LVIS, Obj365
│   ├── InTheWild_v3_v3det_human_only.json       # Train (Human) — V3Det
│   ├── InTheWild_v3_v3det_human.json            # Train (Essential) — V3Det
│   ├── InTheWild_v3_v3det_synthetic.json        # Train (Synthetic) — V3Det
│   └── InTheWild_v3_*_class_map.json            # Category mappings
├── depth/{split}/                               # Monocular depth maps (extract from .tar.gz)
│   └── {source}_{formatted_id}.npz             # float32 .npz at original resolution
├── camera/{split}/                              # Camera parameters (extract from .tar.gz)
│   └── {source}_{formatted_id}.json            # Camera intrinsics (K)
└── images/                                      # Downloaded separately (see Step 2)
    ├── coco_val/
    ├── coco_train/
    ├── obj365_val/
    ├── obj365_train/
    └── v3det_train/

Depth and Camera File Naming

Depth maps and camera parameters are named as {source}_{formatted_id}, where {source} is derived from the image's file_path field in the annotation JSON:

file_path	Depth / Camera filename
images/coco_val/000000000724.jpg	coco_val_000000000724.npz/.json
images/coco_train/000000262686.jpg	coco_train_000000262686.npz/.json
images/obj365_train/obj365_train_000000628903.jpg	obj365_train_000000628903.npz/.json
images/v3det_train/Q100507578/28_284_....jpg	v3det_train_000000000915.npz/.json

Note: Some images from COCO and LVIS share the same underlying image file (LVIS uses COCO images). These appear as separate entries in the annotation JSON (with different annotations) but map to the same depth/camera file. To load the depth/camera for an image entry, extract the source prefix from file_path.split("/")[1] and combine with formatted_id.

# Example: load depth and camera for an image
img = data["images"][0]
source = img["file_path"].split("/")[1]   # e.g., "coco_train"
fid = img["formatted_id"]                 # e.g., "000000262686"

depth = np.load(f"depth/{split}/{source}_{fid}.npz")["depth"]  # float32, (H, W)
camera = json.load(open(f"camera/{split}/{source}_{fid}.json"))

Depth Format

Each .npz file contains a single key "depth" with a float32 2D array at original image resolution (meters).

Camera Format

Each .json file contains:

{
    "K": [[fx, 0, cx], [0, fy, cy], [0, 0, 1]],
    "image_size": [height, width]
}

• K: Camera intrinsic matrix (3x3), at original image resolution
• image_size: [height, width] of the original image

Step 1: Download and Extract

pip install huggingface_hub

# Download only annotations
huggingface-cli download weikaih/WildDet3D-Data --repo-type dataset --include "annotations/*" --local-dir WildDet3D-Data

# Download specific splits (e.g., val only)
huggingface-cli download weikaih/WildDet3D-Data --repo-type dataset --include "packed/depth_val.tar.gz" "packed/camera_val.tar.gz" --local-dir WildDet3D-Data

# Download everything
huggingface-cli download weikaih/WildDet3D-Data --repo-type dataset --local-dir WildDet3D-Data

Extract Depth Maps

Depth maps are provided as compressed archives. Large splits are split into multiple parts.

# Val and Test (small, single file each)
mkdir -p depth && cd depth
tar xzf ../packed/depth_val.tar.gz
tar xzf ../packed/depth_test.tar.gz

# Train Human (2 parts)
tar xzf ../packed/depth_train_human_part000.tar.gz
tar xzf ../packed/depth_train_human_part001.tar.gz

# V3Det Human (single file)
tar xzf ../packed/depth_v3det_human.tar.gz

# V3Det Synthetic (7 parts)
for i in $(seq -w 0 6); do tar xzf ../packed/depth_v3det_synthetic_part0${i}.tar.gz; done

# Train Synthetic (16 parts)
for i in $(seq -w 0 15); do
    part=$(printf "depth_train_synthetic_part%03d.tar.gz" $i)
    tar xzf ../packed/$part
done
cd ..

Extract Camera Parameters

mkdir -p camera && cd camera
for f in ../packed/camera_*.tar.gz; do tar xzf "$f"; done
cd ..

After extraction, you should have depth/{split}/ and camera/{split}/ directories with individual files per image.

Step 2: Download Source Images

Images must be downloaded from their original sources and organized into the following structure:

images/
├── coco_val/          # COCO val2017
├── coco_train/        # COCO train2017 (includes LVIS images)
├── obj365_val/        # Objects365 validation
├── obj365_train/      # Objects365 training
└── v3det_train/       # V3Det training

COCO (val2017 + train2017)

Used by: Val, Test, Train (all splits)

# COCO val2017 — used by Val and Test
wget http://images.cocodataset.org/zips/val2017.zip
unzip val2017.zip
mkdir -p images/coco_val
mv val2017/* images/coco_val/

# COCO train2017 — used by Val/Test for LVIS images, and all Train splits
wget http://images.cocodataset.org/zips/train2017.zip
unzip train2017.zip
mkdir -p images/coco_train
mv train2017/* images/coco_train/

Objects365

Used by: Val, Test (val split), Train (train split)

# Objects365 — download from https://www.objects365.org/
mkdir -p images/obj365_val
# Images should be named: obj365_val_000000XXXXXX.jpg

mkdir -p images/obj365_train
# Images should be named: obj365_train_000000XXXXXX.jpg

V3Det

Used by: Train V3Det splits only

# V3Det — download from https://v3det.openxlab.org.cn/
mkdir -p images/v3det_train
# Directory structure: images/v3det_train/{category_folder}/{image}.jpg
# e.g., images/v3det_train/Q100507578/28_284_50119550013_7d06ded882_c.jpg

Source	Directory	Used By
COCO val2017	images/coco_val/	Val, Test
COCO train2017	images/coco_train/	Val, Test, Train
Objects365 val	images/obj365_val/	Val, Test
Objects365 train	images/obj365_train/	Train
V3Det train	images/v3det_train/	Train (V3Det)

For evaluation only (Val + Test): you only need COCO val2017, COCO train2017, and Objects365 val.

Annotation Format (COCO3D)

Each annotation JSON follows the COCO3D format:

{
  "info": {"name": "InTheWild_v3_val"},
  "images": [{
    "id": 0,
    "width": 375,
    "height": 500,
    "file_path": "images/coco_val/000000000724.jpg",
    "K": [[fx, 0, cx], [0, fy, cy], [0, 0, 1]]
  }],
  "categories": [{"id": 0, "name": "stop sign"}],
  "annotations": [{
    "id": 0,
    "image_id": 0,
    "category_id": 0,
    "category_name": "stop sign",
    "bbox2D_proj": [x1, y1, x2, y2],
    "center_cam": [cx, cy, cz],
    "dimensions": [width, height, length],
    "R_cam": [[r00, r01, r02], [r10, r11, r12], [r20, r21, r22]],
    "bbox3D_cam": [[x, y, z], ...],
    "valid3D": true
  }]
}

Image fields:

• K: Camera intrinsic matrix (3x3), at original image resolution
• file_path: Relative path to the source image

Annotation fields:

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

Which Files to Use

Use Case	Annotation Files
Evaluation	InTheWild_v3_val.json, InTheWild_v3_test.json
Train (Human only)	InTheWild_v3_train_human_only.json + InTheWild_v3_v3det_human_only.json
Train (Essential)	InTheWild_v3_train_human.json + InTheWild_v3_v3det_human.json
Train (Synthetic)	InTheWild_v3_train_synthetic.json + InTheWild_v3_v3det_synthetic.json
Train (All)	Essential + Synthetic (all 4 files)

License

• Annotations: CC BY 4.0