VisionDrive3D: Synthetic Driving Dataset

VisionDrive3D is a synthetic urban driving dataset rendered with a custom OpenGL engine. Every scene comes with an RGB image, a dense metric depth map, a segmentation mask, and annotations in COCO, YOLO, and KITTI (3D) formats. It also includes per-scene camera parameters (intrinsics and extrinsics) and object metadata.

• Code & project page: https://github.com/BenBunBo/VisionDrive3D
• File: VisonDrive3D_testdata.zip (~1.1 GB compressed, ~6.6 GB extracted)

Summary

Property	Value
Scenes	1,000
Resolution	1600 × 900
Annotated class	car (2,585 instances)
Splits	train 800 / val 100 / test 100
Modalities	RGB, depth (float32, metres), segmentation mask
Label formats	COCO JSON, YOLO TXT, KITTI 3D TXT
Camera	Pinhole, fx = fy = 1200, cx = 800, cy = 450, FOV 45°, near 0.1, far 150

Installation

1. Download

Option A: hf CLI (recommended)

pip install -U huggingface_hub
hf download BenBunBo/VisionDrive3D VisonDrive3D_testdata.zip \
    --repo-type dataset --local-dir ./data

Option B: Python

from huggingface_hub import hf_hub_download

zip_path = hf_hub_download(
    repo_id="BenBunBo/VisionDrive3D",
    filename="VisonDrive3D_testdata.zip",
    repo_type="dataset",
    local_dir="./data",
)
print(zip_path)

Option C: Direct link / git

wget https://huggingface.co/datasets/BenBunBo/VisionDrive3D/resolve/main/VisonDrive3D_testdata.zip
# or
git lfs install
git clone https://huggingface.co/datasets/BenBunBo/VisionDrive3D

2. Extract

The archive has no top-level folder, so extract it into its own directory:

mkdir -p data/output_dataset
unzip data/VisonDrive3D_testdata.zip -d data/output_dataset

import zipfile
zipfile.ZipFile("data/VisonDrive3D_testdata.zip").extractall("data/output_dataset")

3. Verify

ls data/output_dataset
# depth  images  ImageSets  labels  masks  metadata  README.txt
ls data/output_dataset/images | wc -l    # 1000
wc -l data/output_dataset/ImageSets/*.txt # 800 / 100 / 100

4. Use with the VisionDrive3D code (optional)

git clone https://github.com/BenBunBo/VisionDrive3D
cd VisionDrive3D
# Put the extracted folder at ./output_dataset (the default path used by the scripts)
ln -s /path/to/data/output_dataset ./output_dataset
python src/ai_demo/run_all.py --dataset ./output_dataset

Folder structure

output_dataset/
├── README.txt
├── images/              # RGB renders            scene_XXXXX.png  (1600×900, 8-bit RGB)
├── depth/               # Depth maps             scene_XXXXX.npy  (900×1600, float32, metres)
├── masks/               # Segmentation masks     scene_XXXXX.png  (1600×900, 8-bit RGB colour-coded)
├── labels/
│   ├── coco/            # COCO JSON              scene_XXXXX.json
│   ├── yolo/            # YOLO TXT               scene_XXXXX.txt
│   └── kitti/           # KITTI 3D TXT           scene_XXXXX.txt
├── metadata/
│   ├── scene_XXXXX.json # Camera + per-object metadata
│   └── dataset_log.csv  # One row per scene (camera position, depth range, #objects, ...)
└── ImageSets/
    ├── train.txt        # 800 scene ids
    ├── val.txt          # 100 scene ids
    └── test.txt         # 100 scene ids

Scenes are named scene_00000 … scene_00999. Each file in ImageSets/ lists one 5-digit id per line (e.g. 00042 → scene_00042).

Annotation formats

YOLO (labels/yolo/*.txt): one object per line, normalised to [0, 1]:

<class_id> <x_center> <y_center> <width> <height>
0 0.976250 0.516667 0.047500 0.044444

Class 0 = car.

COCO (labels/coco/*.json): one JSON file per scene with images, annotations (bbox as [x, y, w, h] in pixels, polygon segmentation, area) and categories. The category list contains car (0), traffic_light (1), traffic_sign (2), pedestrian (3) and entity (99), but only car is annotated in this release. Note: images[].file_name has the output_dataset/ prefix, e.g. output_dataset/images/scene_00000.png.

KITTI (labels/kitti/*.txt): standard KITTI object label format, 15 fields per line:

type truncated occluded alpha  x1 y1 x2 y2  h w l  x y z  rotation_y
Car  1.00      0        2.15   1524 445 1600 485  1.88 2.16 4.88  37.32 1.66 56.71  1.57

Dimensions and location are in metres in camera coordinates. The camera intrinsics are in metadata/scene_XXXXX.json → camera.intrinsic_matrix.

Depth (depth/*.npy): float32 array of shape (900, 1600) with per-pixel depth in metres, clipped to the far plane (150 m).

Metadata (metadata/scene_XXXXX.json): scene_id, timestamp, image_path, camera (position, target, up, fov_deg, near, far, intrinsic_matrix, extrinsic_matrix), objects (class, world pose, scale, 2D/3D boxes, visibility, occlusion ratio), render_config, and stats.

Quick start

import json
import numpy as np
from PIL import Image

root = "data/output_dataset"
sid = "scene_00000"

rgb   = np.array(Image.open(f"{root}/images/{sid}.png"))   # (900, 1600, 3) uint8
depth = np.load(f"{root}/depth/{sid}.npy")                  # (900, 1600) float32, metres
mask  = np.array(Image.open(f"{root}/masks/{sid}.png"))    # (900, 1600, 3) uint8
meta  = json.load(open(f"{root}/metadata/{sid}.json"))
K     = np.array(meta["camera"]["intrinsic_matrix"])        # 3×3

with open(f"{root}/labels/yolo/{sid}.txt") as f:
    boxes = [list(map(float, line.split())) for line in f]

train_ids = open(f"{root}/ImageSets/train.txt").read().split()
print(rgb.shape, depth.min(), depth.max(), len(boxes), len(train_ids))

Train YOLO (Ultralytics): write a data.yaml that points at the split lists:

from pathlib import Path

root = Path("data/output_dataset").resolve()
for split in ["train", "val", "test"]:
    ids = (root / "ImageSets" / f"{split}.txt").read_text().split()
    (root / f"{split}_images.txt").write_text(
        "\n".join(str(root / "images" / f"scene_{i}.png") for i in ids)
    )

YOLO looks for labels next to the images (images/ → labels/), so copy or symlink labels/yolo/*.txt into a sibling labels/ folder of the image directory, or use the conversion scripts in the GitHub repo.

Citation

@misc{visiondrive3d2026,
  title  = {VisionDrive3D: Synthetic Driving Data for Detection and Segmentation},
  author = {BenBunBo},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/datasets/BenBunBo/VisionDrive3D}}
}