FlowFormer: 3D scene flow estimation for point clouds with transformers

Since estimating scene flow from point clouds is challenging, some methods involve the robust Transformer. However, there are two problems with these methods: (1) Dense connectivity of global attention reaches the efficiency bottleneck. (2) The lack of adaptive flow encoding in local neighborhoods results in inaccurate flow values. To this end, we propose a new Transformer based scene flow estimation network. Specifically, we first develop a lightweight feature enhancement module with seed points guided attention layers to encode the global contexts of point clouds into local descriptors, so that discriminative descriptors are generated for reliable correlation construction and initial flow estimation. Then, we construct a Transformer based flow refinement module for more accurate flow, which includes a flow aggregation layer at the coarse level and a flow propagation layer at the finer level. The flow aggregation layer encodes the local flow smoothness via adaptively aggregating the flow of neighbors to central points. The flow propagation layer can softly upsample the coarse-level flow to the finer level, which directly takes the coarse-level flow as the “value” and adaptively learns the transform weights between the coarse-level points and fine-level points. The experiments on extensive datasets demonstrate that our method can yield outstanding performance.