Visual Object Categorization Based on Hierarchical Shape Motifs Learned From Noisy Point Cloud Decompositions

Abstract

Object shape is a key cue that contributes to the semantic understanding of objects. In this work we focus on the categorization of real-world object point clouds to particular shape types. Therein surface description and representation of object shape structure have significant influence on shape categorization accuracy, when dealing with real-world scenes featuring noisy, partial and occluded object observations. An unsupervised hierarchical learning procedure is utilized here to symbolically describe surface characteristics on multiple semantic levels. Furthermore, a constellation model is proposed that hierarchically decomposes objects. The decompositions are described as constellations of symbols (shape motifs) in a gradual order, hence reflecting shape structure from local to global, i.e., from parts over groups of parts to entire objects.The combination of this multi-level description of surfaces and the hierarchical decomposition of shapes leads to a representation which allows to conceptualize shapes. An object discrimination has been observed in experiments with seven categories featuring instances with sensor noise, occlusions as well as inter-category and intra-category similarities. Experiments include the evaluation of the proposed description and shape decomposition approach, and comparisons to Fast Point Feature Histograms, a Vocabulary Tree and a neural network-based Deep Learning method. Furthermore, experiments are conducted with alternative datasets which analyze the generalization capability of the proposed approach.

Denoising for 3D Point Cloud Based on Regularization of a Statistical Low-Dimensional Manifold

“…Due to their simplicity, flexibility and strong representative ability, three-dimensional (3D) point clouds are more and more widely used in many fields [ 1 , 2 , 3 ], such as object recognition and surface reconstruction. Point cloud data are mainly obtained in two ways [ 4 , 5 ], namely using a stereo matching algorithm or a 3D scanner.…”