An online vision system for understanding complex assembly tasks

This paper addresses the detection of geometric edges on 3D shapes. We investigate the use of local point cloud features and cast the edge detection problem as a learning problem. We show how supervised learning techniques can be applied to an existing shape description in terms of local feature descriptors. We apply our approach to several well-known shape descriptors. As an additional contribution, we develop a novel shape descriptor, termed
Equivalent Circumference Surface Angle Descriptor or ECSAD, which is particularly suitable for capturing local surface properties near edges. Our proposed descriptor allows for both fast computation and fast processing by having a low dimension, while still producing highly reliable edge detections. Lastly, we use our features in a 3D object recognition application using a well-established benchmark. We show that our edge features allow for significant speedups while achieving state of the art results.