In this paper we present a method to combine the detection and segmentation of object categories from 3D scenes. In the process, we combine the top-down cues available from object detection technique of Implicit Shape Models and the bottom-up power of Markov Random Fields for the purpose of segmentation. While such approaches have been tried for the 2D image problem domain before, this is the first application of such a method in 3D. 3D scene understanding is prone to many problems different from 2D owing to problems from noise, lack of distinctive high-frequency feature information, mesh parametrization problems etc. Our method enables us to localize objects of interest for more purposeful meshing and subsequent scene understanding.
['Jan Knopp', 'Mukta Prasad', 'Luc J. Van Gool']
Scene Cut: Class-Specific Object Detection and Segmentation in 3D Scenes