A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion

Understand

3D point cloud is an important 3D representation for capturing real world 3D objects.

• However, real-scanned 3D point clouds are often incomplete, and it is important to recover complete point clouds for downstream applications.
• Most existing point cloud completion methods use Chamfer Distance (CD) loss for training.
• The CD loss estimates correspondences between two point clouds by searching nearest neighbors, which does not capture the overall point density distribution on the generated shape, and therefore likely leads to non-uniform point cloud generation.