Spatial Pruned Sparse Convolution for Efficient 3D Object Detection
This work addresses computational bottlenecks in 3D object detection for autonomous driving, offering an incremental improvement by integrating into existing sparse 3D CNNs.
The paper tackles the computational inefficiency of 3D CNNs in object detection by proposing spatial pruned sparse convolution (SPS-Conv), which reduces GFLOPs by over 50% without performance loss on datasets like KITTI, Waymo, and nuScenes.
3D scenes are dominated by a large number of background points, which is redundant for the detection task that mainly needs to focus on foreground objects. In this paper, we analyze major components of existing sparse 3D CNNs and find that 3D CNNs ignore the redundancy of data and further amplify it in the down-sampling process, which brings a huge amount of extra and unnecessary computational overhead. Inspired by this, we propose a new convolution operator named spatial pruned sparse convolution (SPS-Conv), which includes two variants, spatial pruned submanifold sparse convolution (SPSS-Conv) and spatial pruned regular sparse convolution (SPRS-Conv), both of which are based on the idea of dynamically determining crucial areas for redundancy reduction. We validate that the magnitude can serve as important cues to determine crucial areas which get rid of the extra computations of learning-based methods. The proposed modules can easily be incorporated into existing sparse 3D CNNs without extra architectural modifications. Extensive experiments on the KITTI, Waymo and nuScenes datasets demonstrate that our method can achieve more than 50% reduction in GFLOPs without compromising the performance.