PCDreamer: Point Cloud Completion Through Multi-view Diffusion Priors

a) Input (b) Input of other view (c) CRA-PCN [31] (d) SVDFormer [58] (e) Ours (f) GT Figure 1. Given a partial point cloud input (a) with (b) as a novel view for visualization purposes, the goal of point cloud completion is to produce a complete point cloud retaining both the global and local geometric features. Existing methods (c, d) fail to neither recover local thin structures (e.g., the lamp holder) nor capture the global symmetric parts (e.g., the back supporter of the chair), while our approach (e) faithfully produces the desired shape compared with the ground truth (f).