Ada3D : Exploiting the Spatial Redundancy with Adaptive Inference for Efficient 3D Object Detection. (arXiv:2307.08209v2 [cs.CV] UPDATED)

Voxel-based methods have achieved state-of-the-art performance for 3D object
detection in autonomous driving. However, their significant computational and
memory costs pose a challenge for their application to resource-constrained
vehicles. One reason for this high resource consumption is the presence of a
large number of redundant background points in Lidar point clouds, resulting in
spatial redundancy in both 3D voxel and dense BEV map representations. To
address this issue, we propose an adaptive inference framework called Ada3D,
which focuses on exploiting …

3d object detection application art arxiv autonomous autonomous driving challenge computational costs detection driving inference memory performance reason redundancy state voxel