Recent weakly-supervised methods for scene flow estimation from LiDAR point clouds are limited to explicit reasoning on object-level. These methods perform multiple iterative optimizations for each rigid object, which makes them vulnerable to clustering robustness. In this paper, we propose our EgoFlowNet - a point-level scene flow estimation network trained in a weakly-supervised manner and without object-based abstraction. Our approach predicts a binary segmentation mask that implicitly drives two parallel branches for ego-motion and scene flow. Unlike previous methods, we provide both branches with all input points and carefully integrate the binary mask into the feature extraction and losses. We also use a shared cost volume with local refinement that is updated at multiple scales without explicit clustering or rigidity assumptions. On realistic KITTI scenes, we show that our EgoFlowNet performs better than state-of-the-art methods in the presence of ground surface points.

我们提出了EgoFlowNet，一种在弱监督方式下训练的基于点的场景流估计网络，不需要基于对象的抽象，通过预测二值分割掩模隐式驱动两个平行分支，分别用于自运动和场景流，与传统方法不同，我们将二值掩模与特征提取和损失函数精心整合，并利用更新的多尺度共享代价体积进行局部细化，不需要显式聚类或刚体假设，在真实的KITTI场景中，在地表点存在的情况下，我们的EgoFlowNet表现优于最先进的方法。

EgoFlowNet：具有自我运动支持的点云非刚性场景流