Model Globally, Match Locally: Efficient and Robust 3D Object Recognition

This paper addresses the problem of recognizing freeform 3D objects in point clouds. Compared to traditional approaches based on point descriptors, which depend on local information around points, we propose a novel method that creates a global model description based on oriented point pair features and matches that model locally using a fast voting scheme. The global model description consists of all model point pair features and represents a mapping from the point pair feature space to the model, where similar features on the model are grouped together. Such representation allows using much sparser object and scene point clouds, resulting in very fast performance. Recognition is done locally using an efficient voting scheme on a reduced two-dimensional search space. We demonstrate the efficiency of our approach and show its high recognition performance in the case of noise, clutter and partial occlusions. Compared to state of the art approaches we achieve better recognition rates, a...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Bertram Drost, Markus Ulrich, Nassir Navab, Slobodan Ilic
• Keywords: Computer Vision, CVPR 2010, Global Model Description, Point Clouds, Point Pair Feature