A Correlated Parts Model for Object Detection in Large 3D Scans
Martin Sunkel, Silke Jansen, Michael D. Wand, Hans‐Peter Seidel · Computer Graphics Forum · 2013
Abstract This paper addresses the problem of detecting objects in 3D scans according to object classes learned from sparse user annotation. We model objects belonging to a class by a set of fully correlated parts, encoding dependencies between local shapes of different parts as well as their relative spatial arrangement. For an efficient and comprehensive retrieval of instances belonging to a class of interest, we introduce a new approximate inference scheme and a corresponding planning procedure. We extend our technique to hierarchical composite structures, reducing training effort and modeling spatial relations between detected instances. We evaluate our method on a number of real‐world 3D scans and demonstrate its benefits as well as the performance of the new inference algorithm.