Cy-CNN: cylinder convolution based rotation-invariant neural network for point cloud registration

Point cloud registration is a challenging problem in the condition of large initial misalignments and noises. A major problem encountered in the registration algorithms is the definition of correspondence between two point clouds. Point clouds contain rich geometric information and the same geometric structure implies the same feature even if they are in different poses, which motivates us to seek a rotation-invariant feature representation for calculating the correspondence. This work proposes a rotation-invariant neural network for point cloud registration. To acquire rotation-invariant features, we firstly propose a rotation-invariant point cloud representation (RIPR) at the input level. Instead of using the original coordinates, we propose to use point pair features (PPF) and the transformed coordinates in the local reference frame (LRF) to represent a point. Then, we design a new convolution operator named Cylinder-Conv which utilizes the symmetry of cylinder-shaped voxels and the hierarchical geometry information of the surface of 3D shapes. By specifying the cylinder-shaped structures and directions, Cylinder-Conv can better capture the local neighborhood geometry of each point and maintain rotation-invariance. Finally, we combine RIPR and Cylinder-Conv to extract normalized rotation-invariant features to generate the correspondence and perform a differentiable singular value decomposition (SVD) step to estimate the rigid transformation. The proposed network presents state-of-the-art performance on point cloud registration. Experiments show that our method is robust to initial misalignments and noises.