  We propose a novel scene flow estimation approach to capture and infer 3D
motions from point clouds. Estimating 3D motions for point clouds is
challenging, since a point cloud is unordered and its density is significantly
non-uniform. Such unstructured data poses difficulties in matching
corresponding points between point clouds, leading to inaccurate flow
estimation. We propose a novel architecture named Sparse
Convolution-Transformer Network (SCTN) that equips the sparse convolution with
the transformer. Specifically, by leveraging the sparse convolution, SCTN
transfers irregular point cloud into locally consistent flow features for
estimating continuous and consistent motions within an object/local object
part. We further propose to explicitly learn point relations using a point
transformer module, different from exiting methods. We show that the learned
relation-based contextual information is rich and helpful for matching
corresponding points, benefiting scene flow estimation. In addition, a novel
loss function is proposed to adaptively encourage flow consistency according to
feature similarity. Extensive experiments demonstrate that our proposed
approach achieves a new state of the art in scene flow estimation. Our approach
achieves an error of 0.038 and 0.037 (EPE3D) on FlyingThings3D and KITTI Scene
Flow respectively, which significantly outperforms previous methods by large
margins.
