IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes
Shubham Dokania, A.H. Abdul Hafez, Anbumani Subramanian, Manmohan, Chandraker, C.V. Jawahar

TL;DR
The paper introduces IDD-3D, a large-scale, multi-modal dataset capturing complex unstructured road scenes in India, aimed at improving autonomous driving models' adaptability to diverse and challenging real-world traffic conditions.
Contribution
The creation of a comprehensive, geographically diverse 3D driving dataset from India with multi-modal data, addressing limitations of existing datasets biased towards developed cities.
Findings
IDD-3D contains 12,000 annotated LiDAR frames.
Benchmark results on 3D detection and tracking demonstrate the dataset's complexity.
Statistical analysis shows IDD-3D's diversity exceeds existing datasets.
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…
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Videos
IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes· youtube
Taxonomy
TopicsAdvanced Neural Network Applications · Autonomous Vehicle Technology and Safety · Traffic Prediction and Management Techniques
