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Geometry driven semantic labeling of indoor scenes

Khan, S.H., Bennamoun, M., Sohel, F. and Togneri, R. (2014) Geometry driven semantic labeling of indoor scenes. Lecture Notes in Computer Science, 8689 . pp. 679-694.

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We present a discriminative graphical model which integrates geometrical information from RGBD images in its unary, pairwise and higher order components. We propose an improved geometry estimation scheme which is robust to erroneous sensor inputs. At the unary level, we combine appearance based beliefs defined on pixels and planes using a hybrid decision fusion scheme. Our proposed location potential gives an improved representation of the planar classes. At the pairwise level, we learn a balanced combination of various boundaries to consider the spatial discontinuity. Finally, we treat planar regions as higher order cliques and use graphcuts to make efficient inference. In our model based formulation, we use structured learning to fine tune the model parameters. We test our approach on two RGBD datasets and demonstrate significant improvements over the state-of-the-art scene labeling techniques.

Publication Type: Journal Article
Publisher: Springer Verlag
Copyright: 2014 Springer International Publishing Switzerland
Notes: Book: Computer Vision – ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I
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