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Complete point clouds using prior knowledge and causal inference with Point-PC

Complete point clouds using prior knowledge and causal inference with Point-PC


Point-PC: Point Cloud Completion Guided by Prior Knowledge via Causal Inference

arXiv paper abstract https://arxiv.org/abs/2305.17770



Point cloud completion aims to recover raw point clouds captured by scanners from partial observations caused by occlusion and limited view angles.


... propose a novel approach ... called Point-PC, which uses a memory network to retrieve shape priors and designs an effective causal inference model to choose missing shape information as additional geometric information to aid point cloud completion.


... propose a memory operating mechanism where the complete shape features and the corresponding shapes are stored in the form of ``key-value'' pairs.


To retrieve similar shapes from the partial input ... apply a contrastive learning-based pre-training scheme to transfer features of incomplete shapes into the domain of complete shape features.


... use backdoor adjustment to get rid of the confounder, which is a part of the shape prior that has the same semantic structure as the partial input.


... Point-PC performs favorably against the state-of-the-art methods.



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