  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.
