Update README.md

README.md

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 ---
 license: mit
+tags:
+  - video
+  - driving
+  - Bengaluru
+  - disparity maps
+  - depth dataset
+homepage: https://adityang.github.io/AdityaNG/BengaluruDrivingDataset/
 ---
+
+# Bengaluru Semantic Occupancy 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}
+}