Rethinking Dimensionality Reduction in Grid-based 3D Object Detection
This work addresses a key bottleneck in 3D object detection for autonomous driving by improving feature representation, though it is incremental as it builds on existing BEV-based methods.
The paper tackles the problem of information loss in bird's eye view (BEV) feature extraction for 3D object detection by proposing MDRNet, which uses a multi-level dimensionality reduction strategy to preserve spatial information, achieving state-of-the-art performance on the nuScenes dataset.
Bird's eye view (BEV) is widely adopted by most of the current point cloud detectors due to the applicability of well-explored 2D detection techniques. However, existing methods obtain BEV features by simply collapsing voxel or point features along the height dimension, which causes the heavy loss of 3D spatial information. To alleviate the information loss, we propose a novel point cloud detection network based on a Multi-level feature dimensionality reduction strategy, called MDRNet. In MDRNet, the Spatial-aware Dimensionality Reduction (SDR) is designed to dynamically focus on the valuable parts of the object during voxel-to-BEV feature transformation. Furthermore, the Multi-level Spatial Residuals (MSR) is proposed to fuse the multi-level spatial information in the BEV feature maps. Extensive experiments on nuScenes show that the proposed method outperforms the state-of-the-art methods. The code will be available upon publication.