 |
    |
Geometry Driven Semantic Labeling of Indoor Scenes*
Salman Hameed Khan1, Mohammed Bennamoun1, Ferdous Sohel1, and Roberto Togneri2
1School of CSSE, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
2School of EECE, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
Abstract. 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.
Electronic Supplementary Material:
LNCS 8689, p. 679 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014