Low-resolution point clouds are challenging for object detection methods due to their sparsity. Densifying the present point cloud by concatenating it with its predecessors is a popular solution to this challenge. Such concatenation is possible thanks to the removal of ego vehicle motion using its odometry. This method is called Ego Motion Compensation (EMC). Thanks to the added points, EMC significantly improves the performance of single-frame detectors. However, it suffers from the shadow effect that manifests in dynamic objects' points scattering along their trajectories. This effect results in a misalignment between feature maps and objects' locations, thus limiting performance improvement to stationary and slow-moving objects only. Scene flow allows aligning point clouds in 3D space, thus naturally resolving the misalignment in feature spaces. By observing that scene flow computation shares several components with 3D object detection pipelines, we develop a plug-in module that enables single-frame detectors to compute scene flow to rectify their Bird-Eye View representation. Experiments on the NuScenes dataset show that our module leads to a significant increase (up to 16%) in the Average Precision of large vehicles, which interestingly demonstrates the most severe shadow effect. The code is published at https://github.com/quan-dao/pc-corrector.

翻译：低分辨率点云因其稀疏性对目标检测方法构成挑战。将当前点云与历史点云拼接以增加密度是应对该挑战的常用解决方案。利用车辆里程计移除自车运动后即可实现此类拼接，该方法称为自车运动补偿（EMC）。通过添加历史点云，EMC显著提升了单帧检测器的性能，但其存在“阴影效应”——动态目标的点云会沿其运动轨迹扩散。该效应导致特征图与目标位置错位，因此仅能改善静止与慢速目标的检测性能。场景流可在三维空间中对齐点云，从而从根源上解决特征空间的错位问题。通过发现场景流计算与3D目标检测流程共享多个组件，我们开发了一种即插即用模块，使单帧检测器能够计算场景流以校正其鸟瞰图表示。在NuScenes数据集上的实验表明，该模块使大型车辆的平均精度提升了最高16%——大型车辆恰恰呈现出最严重的阴影效应。相关代码已发布于https://github.com/quan-dao/pc-corrector。