Fast and Accurate Normal Estimation for Point Cloud via Patch Stitching
- URL: http://arxiv.org/abs/2103.16066v2
- Date: Wed, 31 Mar 2021 05:18:51 GMT
- Title: Fast and Accurate Normal Estimation for Point Cloud via Patch Stitching
- Authors: Jun Zhou, Wei Jin, Mingjie Wang, Xiuping Liu, Zhiyang Li and Zhaobin
Liu
- Abstract summary: We present an effective normal estimation method adopting multi-patch stitching for an unstructured point cloud.
Our method achieves SOTA results with the advantage of lower computational costs and higher robustness to noise over most of the existing approaches.
- Score: 12.559091712749279
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 of the existing approaches.
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