WalkFormer: Point Cloud Completion via Guided Walks

Point clouds are often sparse and incomplete in real-world scenarios. The prevailing methods for point cloud completion typically rely on encoding the partial points and then decoding complete points from a global feature vector, which might lose the existing patterns and elaborate structures. To address these issues, we propose WalkFormer, a novel approach to predict complete point clouds through a partial deformation process. Concretely, our method samples locally dominant points based on feature similarity and moves the points to form the missing part. Since these points maintain representative information of the surrounding structures, they are appropriately selected as the starting points for multiple guided walks. Furthermore, we design a Route Transformer module to exploit and aggregate the walk information with topological relations. These guided walks facilitate the learning of long-range dependencies for predicting shape deformation. Qualitative and quantitative evaluations demonstrate that our proposed approach achieves superior performance compared to state-of-the-art methods in the 3D point cloud completion task.