  3D point cloud completion is designed to recover complete shapes from
partially observed point clouds. Conventional completion methods typically
depend on extensive point cloud data for training %, with their effectiveness
often constrained to object categories similar to those seen during training.
In contrast, we propose a zero-shot framework aimed at completing partially
observed point clouds across any unseen categories. Leveraging point rendering
via Gaussian Splatting, we develop techniques of Point Cloud Colorization and
Zero-shot Fractal Completion that utilize 2D priors from pre-trained diffusion
models to infer missing regions. Experimental results on both synthetic and
real-world scanned point clouds demonstrate that our approach outperforms
existing methods in completing a variety of objects without any requirement for
specific training data.
