Rethinking Dimensionality Reduction in Grid-based 3D Object Detection

• URL: http://arxiv.org/abs/2209.09464v1
• Date: Tue, 20 Sep 2022 04:51:54 GMT
• Title: Rethinking Dimensionality Reduction in Grid-based 3D Object Detection
• Authors: Dihe Huang, Ying Chen, Yikang Ding, Jinli Liao, Jianlin Liu, Kai Wu, Qiang Nie, Yong Liu, Chengjie Wang
• Abstract summary: 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. Experiments on nuScenes show that the proposed method outperforms the state-of-the-art methods.
• Score: 24.249147412551768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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