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

Edge and Corner Detection for Unorganized 3D Point Clouds with Application to Robotic Welding