  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.
