Continuous Surface-Point Distributions for 3D Object Pose Estimation and Recognition

Abstract

AbstractWe present a 3D, probabilistic object-surface model, along with mechanisms for probabilistically integrating unregistered 2.5D views into the model, and for segmenting model instances in cluttered scenes. The object representation is a probabilistic expression of object parts through smooth surface-point distributions obtained by kernel density estimation on 3D point clouds. A multi-part, viewpoint-invariant model is learned incrementally from a set of roughly segmented, unregistered views, by sequentially registering and fusing the views with the incremental model. Registration is conducted by nonparametric inference of maximum-likelihood model parameters, using Metropolis–Hastings MCMC with simulated annealing. The learning of viewpoint-invariant models and the applicability of our method to pose estimation, object detection, and object recognition is demonstrated on 3D-scan data, providing qualitative, quantitative and comparative evaluations.KeywordsSimulated AnnealingObject RecognitionObject DetectionObject RepresentationKernel Density EstimationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.