LiDAR-based 3D object detection is of paramount importance for autonomous driving. Recent trends show a remarkable improvement for bird's-eye-view (BEV) based and point-based methods as they demonstrate superior performance compared to range-view counterparts. This paper presents an insight that leverages range-view representation to enhance 3D points for accurate 3D object detection. Specifically, we introduce a Redemption from Range-view Module (R2M), a plug-and-play approach for 3D surface texture enhancement from the 2D range view to the 3D point view. R2M comprises BasicBlock for 2D feature extraction, Hierarchical-dilated (HD) Meta Kernel for expanding the 3D receptive field, and Feature Points Redemption (FPR) for recovering 3D surface texture information. R2M can be seamlessly integrated into state-of-the-art LiDAR-based 3D object detectors as preprocessing and achieve appealing improvement, e.g., 1.39%, 1.67%, and 1.97% mAP improvement on easy, moderate, and hard difficulty level of KITTI val set, respectively. Based on R2M, we further propose R2Detector (R2Det) with the Synchronous-Grid RoI Pooling for accurate box refinement. R2Det outperforms existing range-view-based methods by a significant margin on both the KITTI benchmark and the Waymo Open Dataset. Codes will be made publicly available.

翻译：基于激光雷达的3D目标检测对自动驾驶至关重要。近期研究表明，鸟瞰图方法和点云方法相较于距离视图方法展现出更优性能。本文提出利用距离视图表示增强3D点云以实现精确3D目标检测的新思路。具体而言，我们引入了距离视图优化模块（R2M），这是一种从2D距离视图到3D点云视图进行3D表面纹理增强的即插即用方法。R2M包含用于2D特征提取的基本块、用于扩展3D感受野的层级膨胀元内核以及用于恢复3D表面纹理信息的特征点优化模块。该模块可作为预处理步骤无缝集成到当前最先进的基于激光雷达的3D目标检测器中，并取得显著性能提升——例如在KITTI验证集的简单、中等和困难难度级别上分别提升1.39%、1.67%和1.97%的mAP。基于R2M，我们进一步提出采用同步网格RoI池化的R2Detector（R2Det），实现精确的边界框优化。在KITTI基准和Waymo开放数据集上，R2Det均大幅超越现有基于距离视图的方法。相关代码将开源公布。