The remarkable breakthroughs in point cloud representation learning have boosted their usage in real-world applications such as self-driving cars and virtual reality. However, these applications usually have an urgent requirement for not only accurate but also efficient 3D object detection. Recently, knowledge distillation has been proposed as an effective model compression technique, which transfers the knowledge from an over-parameterized teacher to a lightweight student and achieves consistent effectiveness in 2D vision. However, due to point clouds' sparsity and irregularity, directly applying previous image-based knowledge distillation methods to point cloud detectors usually leads to unsatisfactory performance. To fill the gap, this paper proposes PointDistiller, a structured knowledge distillation framework for point clouds-based 3D detection. Concretely, PointDistiller includes local distillation which extracts and distills the local geometric structure of point clouds with dynamic graph convolution and reweighted learning strategy, which highlights student learning on the crucial points or voxels to improve knowledge distillation efficiency. Extensive experiments on both voxels-based and raw points-based detectors have demonstrated the effectiveness of our method over seven previous knowledge distillation methods. For instance, our 4X compressed PointPillars student achieves 2.8 and 3.4 mAP improvements on BEV and 3D object detection, outperforming its teacher by 0.9 and 1.8 mAP, respectively. Codes have been released at https://github.com/RunpeiDong/PointDistiller.

翻译：点云代表制学习的显著突破提高了其在现实世界应用中的使用,如自驾驶汽车和虚拟现实,然而,这些应用通常对精确和高效的3D天体探测有迫切的要求。最近,提出了知识蒸馏作为有效的模型压缩技术,将知识从一个过度分化的教师传授给一个轻量级学生,并在2D愿景中取得一致的效力。然而,由于发现云的偏移性和不规律性,直接应用以前基于图像的知识蒸馏方法来点云探测器通常会导致不令人满意的性能。为填补空白,本文提议了点Distiller,这是基于云的3D探测的结构性知识蒸馏框架。具体地说,点Dmissiller包括当地蒸馏法,它提取和提炼点云的当地几何结构,并用动态图解析和重新加权的学习战略,它强调学生在关键点上学习或氧化素,以提高知识蒸馏效率。基于oxel和基于原始点的Prentral-Preal-boral-cal-brental-cal-procal-