Enhancing Grid-Based 3D Object Detection in Autonomous Driving With Improved Dimensionality Reduction

Abstract

Point cloud object detection is a pivotal technology in autonomous driving and robotics. Currently, the majority of cutting-edge point cloud detectors utilize Bird's Eye View (BEV) for detection, as it allows them to take advantage of well-explored 2D detection techniques. Nevertheless, dimensionality reduction of features from 3D space to BEV space unavoidably leads to information loss, and there is a lack of research on this issue. Existing methods typically obtain BEV features by collapsing voxel or point features along the height dimension via a pooling operation or convolution, resulting in a significant decrease in geometric information. To tackle this problem, we present a new point cloud backbone network for grid-based object detection, MDRNet, which is based on adaptive dimensionality reduction and multi-level spatial residual strategies. In MDRNet, the Spatial-aware Dimensionality Reduction (SDR) is designed to dynamically concentrate on the essential components of the object during 3D-to-BEV transformation. Moreover, the Multi-level Spatial Residuals (MSR) strategy is proposed to effectively fuse multi-level spatial information in BEV feature maps. Our MDRNet can be employed on any existing grid-based object detector, resulting in a remarkable improvement in performance. Numerous experiments conducted on nuScenes, KITTI and DAIR-V have shown that MDRNet surpasses existing SOTA approaches. In particular, on the nuScenes dataset, we attained an impressive 7.2% mAP and 5.0% NDS enhancement compared with CenterPoint.