Pose Estimation and Shape Retrieval with Hough Voting in a Continuous Voting Space

In this paper we present a method for 3D shape classification and pose estimation. Our approach is related to the recently popular adaptations of Implicit Shape Models to 3D data, but differs in some key aspects. We propose to omit the quantization of feature descriptors in favor of a better descriptiveness of training data. Additionally, a continuous voting space, in contrast to discrete Hough spaces in state of the art approaches, allows for more stable classification results under parameter variations. We evaluate and compare the performance of our approach with recently presented methods. The proposed algorithm achieves best results on three challenging datasets for 3D shape retrieval.