Point Cloud Completion via Self-Projected View Augmentation and Implicit Field Constraint

Recent advances in point cloud completion make it possible to simultaneously recover complete shapes and fine details from partial point clouds captured by professional 3D devices, such as Lidar, or consumer cameras, such as iPhones. Despite significant progress, the potential utilization of self-projected views from partial inputs and the effective reduction of noise in generated point clouds remain under-explored. In this paper, we propose a novel point cloud completion method that leverages self-projected view augmentation and implicit field constraints. Specifically, we introduce a cross-view augmentation (CVA) module and a cross-modal fusion (CMF) module to enhance information interaction and integration at the image and modality levels, respectively. We also propose a bidirection-aware refinement block to improve detail and completeness by considering both complete-to-partial detail perception and partial-to-complete structure perception paths. Additionally, we address the issue of noise reduction from the perspective of implicit field constraints. We evaluate our method on several baseline datasets, including PCN, ShapeNet55/34 and KITTI (car). Extensive experiments demonstrate that our method outperforms state-of-the-art methods, achieving improvements of 0.11 CD- $\ell _{1}$ , 0.015 DCD and 0.009 F-score on the standard PCN test set. Furthermore, our approach effectively reduces noise in the generated point clouds, showcasing its promising potential for practical applications.