IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes
This dataset addresses the challenge of adapting autonomous driving models to complex, unstructured traffic scenarios in developing countries like India, though it is incremental as it adds new data rather than proposing novel methods.
The authors tackled the lack of diverse and geographically unbiased datasets for autonomous driving by creating IDD-3D, a new dataset with 12k annotated LiDAR frames from unstructured road scenes in India, and demonstrated its utility through benchmarks on 3D object detection and tracking tasks.
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