  Scene flow estimation, which predicts the 3D motion of scene points from
point clouds, is a core task in autonomous driving and many other 3D vision
applications. Existing methods either suffer from structure distortion due to
ignorance of rigid motion consistency or require explicit pose estimation and
3D object segmentation. Errors of estimated poses and segmented objects would
yield inaccurate rigidity constraints and in turn mislead scene flow
estimation. In this paper, we propose a novel weight-sharing aggregation (WSA)
method for feature and scene flow up-sampling. WSA does not rely on estimated
poses and segmented objects, and can implicitly enforce rigidity constraints to
avoid structure distortion in scene flow estimation. To further exploit
geometric information and preserve local structure, we design a deformation
degree module aim to keep the local region invariance. We modify the
PointPWC-Net and integrate the proposed WSA and deformation degree module into
the enhanced PointPWC-Net to derive an end-to-end scene flow estimation
network, called WSAFlowNet. Extensive experimental results on the
FlyingThings3D and KITTI datasets demonstrate that our WSAFlowNet achieves the
state-of-the-art performance and outperforms previous methods by a large
margin. We will release the source code at
https://github.com/wangyunlhr/WSAFlowNet.git.
