In this paper, we propose novel edge and corner detection algorithms for
unorganized point clouds. Our edge detection method evaluates symmetry in a
local neighborhood and uses an adaptive density based threshold to
differentiate 3D edge points. We extend this algorithm to propose a novel
corner detector that clusters curvature vectors and uses their geometrical
statistics to classify a point as corner. We perform rigorous evaluation of the
algorithms on RGB-D semantic segmentation and 3D washer models from the
ShapeNet dataset and report higher precision and recall scores. Finally, we
also demonstrate how our edge and corner detectors can be used as a novel
approach towards automatic weld seam detection for robotic welding. We propose
to generate weld seams directly from a point cloud as opposed to using 3D
models for offline planning of welding paths. For this application, we show a
comparison between Harris 3D and our proposed approach on a panel workpiece.

本文提出了基于对称性和密度自适应阈值的边缘检测方法和基于聚类曲率向量的角点检测方法，对 RGB-D 语义分割和 3D 模型进行了验证，在机器人焊接的自动化中提出了基于点云的焊缝检测新方法，与 Harris 3D 算法相比证明了其优越性。