  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 the input-level spatial redundancy. Ada3D
adaptively filters the redundant input, guided by a lightweight importance
predictor and the unique properties of the Lidar point cloud. Additionally, we
utilize the BEV features' intrinsic sparsity by introducing the Sparsity
Preserving Batch Normalization. With Ada3D, we achieve 40% reduction for 3D
voxels and decrease the density of 2D BEV feature maps from 100% to 20% without
sacrificing accuracy. Ada3D reduces the model computational and memory cost by
5x, and achieves 1.52x/1.45x end-to-end GPU latency and 1.5x/4.5x GPU peak
memory optimization for the 3D and 2D backbone respectively.
