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
arXiv PDF paper https://arxiv.org/pdf/2305.17770.pdf
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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