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AdityaNG/BengaluruSemanticOccupancyDataset

Bengaluru Semantic Occupancy Dataset 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. Dataset Iterator: https://github.com/AdityaNG/bdd_dataset_iterator Project… See the full description on the dataset page: https://huggingface.co/datasets/AdityaNG/BengaluruSemanticOccupancyDataset.

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Dataset Card

Bengaluru Semantic Occupancy Dataset

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.

—Dataset Iterator: https://github.com/AdityaNG/bdddatasetiterator
—Project Page: https://adityang.github.io/AdityaNG/BengaluruDrivingDataset/
—Dataset Download: https://huggingface.co/datasets/AdityaNG/BengaluruSemanticOccupancyDataset

Paper

Bengaluru Driving Dataset: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarios

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}
}