부산대학교 도서관

The point cloud is disordered and unstructured, and it is difficult to extract detailed features. The detailed part of the target shape is difficult to complete in the point cloud completion task. It proposes a point cloud completion network (BCA-Net) focusing on detail reconstruction, which can reduce noise and refine shapes. Specifically, it utilizes residual deformation architecture to avoid error points. The break and recombine refinement method is used to recover complete point cloud details. In addition, it proposes a bilateral confidence aggregation unit based on recurrent path aggregation to refine the coarse point cloud shape using multiple gating. Our experiments on the ShapeNet and Complete3D datasets demonstrate that our network performs better than other point cloud completion networks. [ABSTRACT FROM AUTHOR]