--- language: - en license: cc-by-nc-4.0 pretty_name: UrbanOmniView task_categories: - keypoint-detection - object-detection tags: - autonomous-driving - 3d-object-detection - bird-eye-view - pose-estimation - synthetic-data - v2x - multi-view - urban - traffic size_categories: - 10K

UrbanOmniView

A Multi-Perspective Dataset for Calibration-Free Monocular 3D Detection of Urban Traffic Participants

Mehmet Kerem Turkcan Devika Gumaste Zoran Kostic
AIDL Lab, Department of Electrical Engineering
Columbia University

UrbanOmniView examples across ego-vehicle, infrastructure, and aerial viewpoints

UrbanOmniView is a dataset for calibration-free monocular 3D object detection across the camera viewpoints found in modern urban sensing: ego-vehicle dashcams, pole-mounted infrastructure cameras, and aerial drones. It combines real-world driving data, real-world infrastructure data, and high-fidelity synthetic data rendered in Unreal Engine 5. Every object is annotated with a 2D bounding box, a class label, and eight ordered keypoints, the projections of its 3D bounding box corners onto the image plane. A single model trained on this format can perform both 2D detection and 3D reasoning without camera intrinsics at inference time. UrbanOmniView was introduced with [UrbanOmniDetect](https://huggingface.co/mehmetkeremturkcan/UrbanOmniDetect) at the CVPR 2026 DriveX workshop. ## Dataset at a Glance | Source | Frames | Viewpoint | Provenance | |:-------|-------:|:----------|:-----------| | KITTI | 15,022 | Ego-vehicle | Converted from the KITTI 3D object benchmark | | DAIR-V2X | 12,424 | Infrastructure | Converted from the DAIR-V2X infrastructure split | | UE5 Synthetic | 10,000 | Ground, infrastructure, drone | Rendered for this work and released here | | **Total** | **37,446** | | | 10% of UrbanOmniView is held out for testing. ## Classes | Class | Description | |:------|:------------| | car | Passenger vehicles, trucks, vans, and buses | | person | Pedestrians | | bike | Bicycles, motorcycles, and scooters | ## Annotation Format Annotations follow the YOLO keypoint format. Each object line contains: - **Class label.** An integer index into the class list above. - **Bounding box.** Normalized center x, center y, width, and height. - **Keypoints.** Eight ordered 2D points (x, y). Indices 0 to 3 are the top corners of the 3D bounding box, and indices 4 to 7 are the bottom corners that touch the ground plane. The keypoint ordering is identical across all viewpoints, which lets a single model learn orientation-aware detection regardless of camera placement. ## Data Sources ### KITTI The KITTI Vision Benchmark Suite provides images and 3D annotations from a car-mounted sensor rig in Karlsruhe, Germany. We use the left-camera images and project the 3D box labels into 2D keypoints with the provided calibration matrices. Calibration is used only to generate labels, never at inference time. ### DAIR-V2X DAIR-V2X is a vehicle-infrastructure cooperative dataset recorded at real intersections in Beijing. We use the infrastructure-side images, captured by elevated pole-mounted cameras looking down at traffic, and project the 3D annotations into 2D keypoints with the provided infrastructure camera parameters. ### UE5 Synthetic We rendered 10,000 frames in the Unreal Engine 5 City Sample. The generation pipeline provides: - **Dynamic environments.** Weather and lighting variation, including rain, snow, and day and night cycles. - **Randomized traffic.** Vehicle, pedestrian, and cyclist assets placed procedurally. - **Multi-viewpoint cameras.** Ground-level, infrastructure-pole, and drone viewpoints sampled by a scripted camera rig. - **Ray-traced rendering.** RGB output at 4K resolution with physically based lighting. - **Automatic annotation.** 3D bounding boxes taken directly from engine object transforms and collision bounds, which yields pixel-accurate projected keypoints. ## Usage Download the dataset: ```bash huggingface-cli download mehmetkeremturkcan/UrbanOmniView --repo-type dataset --local-dir . ``` Dataset configuration for Ultralytics YOLO: ```yaml path: ./urbanomniview/ train: '../.././urbanomniview_train.txt' val: '../.././urbanomniview_val.txt' test: '../.././urbanomniview_test.txt' nc: 3 names: ['car', 'person', 'bike'] kpt_shape: [8, 2] ``` Train with the scripts from the [GitHub repository](https://github.com/mkturkcan/urbanomnidetect): ```bash python train.py --data cfg/dataset/urbanomniview.yaml ``` Or with Ultralytics directly: ```python from ultralytics import YOLO model = YOLO("yolo11x-pose-p2.yaml").load("yolo11x.pt") model.train(data="cfg/dataset/urbanomniview.yaml", imgsz=640, epochs=100) ``` ## Benchmark Results Models trained on UrbanOmniView generalize across all three viewpoint categories. The best configuration, YOLO11x with the P2 feature level at 1920 × 1920, achieves: | Benchmark | Metric | Score | |:----------|:-------|:-----:| | UrbanOmniView val | mAP_50:95 | 0.808 | | KITTI | AP_3D Moderate | 30.71 | | KITTI | AP_BEV Moderate | 35.19 | | DAIR-V2X val | AP @ OKS = 0.50 | 0.938 | Calibration-dependent baselines trained on single-viewpoint data score near zero on viewpoints outside their training distribution. See the paper for detailed comparisons. ## Ethical Considerations - **KITTI and DAIR-V2X** contain real-world street imagery in which faces and license plates may be visible. Users should comply with the original dataset licenses and local privacy regulations. - **UE5 Synthetic** data contains no real individuals. - The dataset is intended for research in autonomous driving, traffic safety, and cooperative perception. We discourage its use for mass surveillance or any application that violates individual privacy. ## Citation ```bibtex @inproceedings{turkcan2026urbanomnidetect, title = {Calibration-Free View-Agnostic Monocular {3D} Object Detection for Urban Scenes}, author = {Turkcan, Mehmet Kerem and Gumaste, Devika and Kostic, Zoran}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, pages = {786--795}, year = {2026} } ``` ## Acknowledgements This work began while the first author was a postdoc in the [Department of Electrical Engineering](https://www.ee.columbia.edu/) ([AIDL Lab](https://www.aidl.ee.columbia.edu/)) at Columbia University. It was supported by the NSF Engineering Research Center for Smart Streetscapes under Award EEC-2133516, NSF Grants CNS-2450567 and CNS-2038984, and by computing resources from the NVIDIA Academic Grant Program and the Empire AI Consortium. ## License The UE5 synthetic portion is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). KITTI and DAIR-V2X remain subject to their original licenses.