| --- |
| license: mit |
| tags: |
| - video |
| - driving |
| - Bengaluru |
| - disparity maps |
| - depth dataset |
| homepage: https://adityang.github.io/AdityaNG/BengaluruDrivingDataset/ |
| --- |
| |
| # Bengaluru Driving Dataset |
|
|
| <img src="https://adityang.github.io/AdityaNG/BengaluruDrivingDataset/index_files/BDD_Iterator_Demo-2023-08-30_08.25.17.gif" > |
|
|
| ## Dataset Summary |
|
|
| We gathered a dataset spanning 114 minutes and 165K frames in Bengaluru, India. Our dataset consists of video data from a calibrated camera sensor with a resolution of 1920×1080 recorded at a framerate of 30 Hz. We utilize a Depth Dataset Generation pipeline that only uses videos as input to produce high-resolution disparity maps. |
|
|
| ## Paper |
|
|
| [Bengaluru Driving Dataset: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarios](https://arxiv.org/abs/2307.10934) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{analgund2023octran, |
| title={Bengaluru Driving Dataset: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarios}, |
| author={Ganesh, Aditya N and Pobbathi Badrinath, Dhruval and |
| Kumar, Harshith Mohan and S, Priya and Narayan, Surabhi |
| }, |
| year={2023}, |
| howpublished={Spotlight Presentation at the Transformers for Vision Workshop, CVPR}, |
| url={https://sites.google.com/view/t4v-cvpr23/papers#h.enx3bt45p649}, |
| note={Transformers for Vision Workshop, CVPR 2023} |
| } |
| |
