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.

翻译：鸟瞰图（BEV）因可利用成熟的2D检测技术而被当前大多数点云检测器广泛采用。然而，现有方法通过简单地将体素或点特征沿高度维度压缩来获取BEV特征，这导致了三维空间信息的严重丢失。为缓解信息损失，我们提出了一种基于多层次特征维度缩减策略的新型点云检测网络，称为MDRNet。在MDRNet中，空间感知维度缩减（SDR）设计用于在体素到BEV特征变换过程中动态聚焦于物体的有价值部分。此外，多层次空间残差（MSR）被提出用于融合BEV特征图中的多层次空间信息。在nuScenes上的大量实验表明，所提方法优于现有最先进方法。代码将在发表后公开。