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 this https URL

研究了利用场景流纠正车辆自运动补偿方法（EMC）中所存在的影子效应， 并提出了一种插件模块，该模块使用场景流来纠正 3D 目标检测的鸟瞰图表示，实验表明，该模块可显著提高大型车辆的平均精度（高达 16％）。