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Found 3 papers, 2 papers with code
IterativePFN: True Iterative Point Cloud Filtering
Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud filtering or denoising.
Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering
Existing methods usually perform normal estimation and filtering separately and often show sensitivity to noise and/or inability to preserve sharp geometric features such as corners and edges.
Deep Point Cloud Normal Estimation via Triplet Learning
It consists of two phases: (a) feature encoding which learns representations of local patches, and (b) normal estimation that takes the learned representation as input and regresses the normal vector.
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