Point-PC: Point cloud completion guided by prior knowledge via causal inference

Abstract

The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints. Numerous methods use a partial-to-complete framework, directly predicting missing components via global characteristics extracted from incomplete inputs. However, this makes detail recovery challenging, as global characteristics fail to provide complete missing component specifics. A new point cloud completion method named Point-PC is proposed. A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion. Concretely, a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format. The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain, enabling retrieval of analogous shapes from incomplete inputs. In addition, the authors employ backdoor adjustment to eliminate confounders, which are shape prior components sharing identical semantic structures with incomplete inputs. Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches. The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.