论文标题
通过知识蒸馏进行有效的3D对象检测
Towards Efficient 3D Object Detection with Knowledge Distillation
论文作者
论文摘要
尽管在3D对象检测中取得了长足的进展，但高级3D检测器通常会遭受大量计算开销。为此，我们探讨了知识蒸馏（KD）开发有效的3D对象探测器的潜力，重点是基于支柱和体素的探测器。在缺乏完善的教师对成对的情况下，我们首先研究了如何从模型压缩和输入降低模型的良好贸易中获得良好贸易和效率的学生模型。然后，我们构建一个基准测试，以评估在六个构建良好的教师成对上，在2D域中开发的3D对象检测中开发的现有KD方法。此外，我们提出了一条改进的KD管道，该管道结合了增强的Logit KD方法，该方法仅在教师分类响应确定的少数关键位置上执行KD，以及教师指导的学生模型初始化，以促进教师模型通过体重遗传转移教师模型向学生传递特征的功能。最后，我们在Waymo数据集上进行了广泛的实验。我们表现最好的模型可实现$ 65.75 \％$ 2级MAPH，超过其教师模型，只需要$ 44 \％的教师拖鞋。我们最有效的型号在NVIDIA A100上运行51 fps，比Pointpillar $ 2.2 \ times $ $，精度更高。代码可在\ url {https://github.com/cvmi-lab/sparsekd}上找到。
Despite substantial progress in 3D object detection, advanced 3D detectors often suffer from heavy computation overheads. To this end, we explore the potential of knowledge distillation (KD) for developing efficient 3D object detectors, focusing on popular pillar- and voxel-based detectors.In the absence of well-developed teacher-student pairs, we first study how to obtain student models with good trade offs between accuracy and efficiency from the perspectives of model compression and input resolution reduction. Then, we build a benchmark to assess existing KD methods developed in the 2D domain for 3D object detection upon six well-constructed teacher-student pairs. Further, we propose an improved KD pipeline incorporating an enhanced logit KD method that performs KD on only a few pivotal positions determined by teacher classification response, and a teacher-guided student model initialization to facilitate transferring teacher model's feature extraction ability to students through weight inheritance. Finally, we conduct extensive experiments on the Waymo dataset. Our best performing model achieves $65.75\%$ LEVEL 2 mAPH, surpassing its teacher model and requiring only $44\%$ of teacher flops. Our most efficient model runs 51 FPS on an NVIDIA A100, which is $2.2\times$ faster than PointPillar with even higher accuracy. Code is available at \url{https://github.com/CVMI-Lab/SparseKD}.
