IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes

• URL: http://arxiv.org/abs/2210.12878v1
• Date: Sun, 23 Oct 2022 23:03:17 GMT
• Title: IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes
• Authors: Shubham Dokania, A.H. Abdul Hafez, Anbumani Subramanian, Manmohan Chandraker, C.V. Jawahar
• Abstract summary: Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios. An unstructured and complex driving layout found in several developing countries such as India poses a challenge to these models. We build a new dataset, IDD-3D, which consists of multi-modal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDAR frames.
• Score: 79.18349050238413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous driving and assistance systems rely on annotated data from traffic and road scenarios to model and learn the various object relations in complex real-world scenarios. Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios and adapt to different situations. Currently, existing datasets, while large-scale, lack such diversities and are geographically biased towards mainly developed cities. An unstructured and complex driving layout found in several developing countries such as India poses a challenge to these models due to the sheer degree of variations in the object types, densities, and locations. To facilitate better research toward accommodating such scenarios, we build a new dataset, IDD-3D, which consists of multi-modal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDAR frames across various traffic scenarios. We discuss the need for this dataset through statistical comparisons with existing datasets and highlight benchmarks on standard 3D object detection and tracking tasks in complex layouts. Code and data available at https://github.com/shubham1810/idd3d_kit.git

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