Point Patches Contrastive Learning for Enhanced Point Cloud Completion

In partial-to-complete point cloud completion, it is imperative that enabling every patch in the output point cloud faithfully represents the corresponding patch in partial input, ensuring similarity in terms of geometric content. To achieve this objective, we propose a straightforward method dubbed PPCL that aims to maximize the mutual information between two point patches from the encoder and decoder by leveraging a contrastive learning framework. Contrastive learning facilitates the mapping of two similar point patches to corresponding points in a learned feature space. Notably, we explore multi-layer point patches contrastive learning (MPPCL) instead of operating on the whole point cloud. The negatives are exploited within the input point cloud itself rather than the rest of the datasets. To fully leverage the local geometries present in the partial inputs and enhance the quality of point patches in the encoder, we introduce Multi-level Feature Learning (MFL) and Hierarchical Feature Fusion (HFF) modules. These modules are also able to facilitate the learning of various levels of features. Moreover, Spatial-Channel Transformer Point Up-sampling (SCT) is devised to guide the decoder to construct a complete and fine-grained point cloud by leveraging enhanced point patches from our point patches contrastive learning. Extensive experiments demonstrate that our PPCL can achieve better quantitive and qualitative performance over off-the-shelf methods across various datasets.