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.

本文提出一种基于多补丁拼接的有效法线估计方法。通过使用局部特征聚合和多支平面专家模块等创新手段，实现在降低计算成本和提高鲁棒性的同时，取得了极具竞争力的效果。