Fast and Accurate Normal Estimation for Point Clouds Via Patch Stitching

Jun Zhou

doi:10.1016/j.cad.2021.103121

Fast and Accurate Normal Estimation for Point Clouds Via Patch Stitching

2022-01-01

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AI

DOI：10.1016/j.cad.2021.103121

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This paper presents an effective normal estimation method adopting multi-patch stitching for an unstructured point cloud. The majority of learning-based approaches encode a local patch around each point of a whole model and estimate the normals in a point-by-point manner. In contrast, we suggest a more efficient pipeline, in which we introduce a patch-level normal estimation architecture to process a series of overlapping patches. Additionally, a multi-normal selection method based on weights, dubbed as multi-patch stitching, integrates the normals from the overlapping patches. To reduce the adverse effects of sharp corners or noise in a patch, we introduce an adaptive local feature aggregation layer to focus on an anisotropic neighborhood. We then utilize a multi-branch planar experts module to break the mutual influence between underlying piecewise surfaces in a patch. At the stitching stage, we use the learned weights of multi-branch planar experts and distance weights between points to select the best normal from the overlapping parts. Furthermore, we put forward constructing a sparse matrix representation to reduce large-scale retrieval overheads for the loop iterations dramatically. Extensive experiments demonstrate that our method achieves SOTA results with the advantage of lower computational costs and higher robustness to noise over most existed approaches. (C) 2021 Elsevier Ltd. All rights reserved.

Keywords:

Normal estimation

Point cloud processing

Patch stitching

Multi-branch planar experts

Adaptive local feature aggregation

Fast and Accurate Normal Estimation for Point Clouds Via Patch Stitching

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