Explaining Point Cloud Segments in Terms of Object Models

Segmenting the signal of a 3D-sensor represents a core problem in computer vision. Describing segments at the object level is a common requirement for higher-level tasks like action recognition. Non-parametric techniques can provide segmentation without prior model information. However, they are also prone to overand under-segmentation, especially in case of high occluded scenes. In this paper we propose an approach to segmenting a 3D scene based on a set of known object models. Six-degree-of-freedom (6DOF) model poses result from recognition and pose estimation by exploiting distinct object shapes acquired from a non-parametric segmentation stream. The aligned object models are used in order to resolve overand under-segmentation by following a bottomup strategy. Segmentation refinement results from contracting and subdividing input segments in accordance to aligned object models. The proposed algorithm is compared to a trivial model-based segmentation approach that neglects the segmentation stream. Both approaches are evaluated on a set of 24 scenes which are divided into four different complexity categories. The complexity of the scenes ranges from simple to advanced, objects are placed in sparse configurations as well as highly occluded compositions.