Multimodal 3D Object Detection on Unseen Domains

arXiv:2404.11764v1 Announce Type: new
Abstract: LiDAR datasets for autonomous driving exhibit biases in properties such as point cloud density, range, and object dimensions. As a result, object detection networks trained and evaluated in different environments often experience performance degradation. Domain adaptation approaches assume access to unannotated samples from the test distribution to address this problem. However, in the real world, the exact conditions of deployment and access to samples representative of the test dataset may be unavailable while training. We …

3d object 3d object detection abstract access arxiv autonomous autonomous driving biases cloud cs.cv datasets detection dimensions distribution domain domain adaptation domains driving environments experience lidar multimodal networks object performance samples test type