Point Cloud Denoising via Moving RPCA

E. Mattei; A. Castrodad

doi:10.1111/cgf.13068

journal article Nov 02, 2016

Point Cloud Denoising via Moving RPCA

E. Mattei A. Castrodad

Computer Graphics Forum Vol. 36 No. 8 pp. 123-137 · Wiley

View at Publisher Save 10.1111/cgf.13068

Abstract

AbstractWe present an algorithm for the restoration of noisy point cloud data, termed Moving Robust Principal Components Analysis (MRPCA). We model the point cloud as a collection of overlapping two‐dimensional subspaces, and propose a model that encourages collaboration between overlapping neighbourhoods. Similar to state‐of‐the‐art sparse modelling‐based image denoising, the estimated point positions are computed by local averaging. In addition, the proposed approach models grossly corrupted observations explicitly, does not require oriented normals, and takes into account both local and global structure. Sharp features are preserved via a weighted ℓ1 minimization, where the weights measure the similarity between normal vectors in a local neighbourhood. The proposed algorithm is compared against existing point cloud denoising methods, obtaining competitive results.

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122

Citations

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References

Details

Published: Nov 02, 2016
Vol/Issue: 36(8)
Pages: 123-137
License: View

Authors

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NGA Springfield VA USA

Cite This Article

E. Mattei, A. Castrodad (2016). Point Cloud Denoising via Moving RPCA. Computer Graphics Forum, 36(8), 123-137. https://doi.org/10.1111/cgf.13068

Point Cloud Denoising via Moving RPCA

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