M. R. Andersen, T. Jensen, P. Lisouski, A. Mortensen, M. Hansen et al., Kinect depth sensor evaluation for computer vision applications, 2012.

J. C. Bazin, Y. Seo, C. Demonceaux, P. Vasseur, K. Ikeuchi et al., Globally optimal line clustering and vanishing point estimation in Manhattan world, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.638-645, 2012.
DOI : 10.1109/CVPR.2012.6247731
URL : https://hal.archives-ouvertes.fr/hal-00697707

J. M. Coughlan and A. L. Yuille, Manhattan World: compass direction from a single image by Bayesian inference, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.941-947, 1999.
DOI : 10.1109/ICCV.1999.790349
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.5078

R. Cupec, E. K. Nyarko, and D. Filko, Fast 2.5d mesh segmentation to approximately convex surfaces, ECMR, pp.49-54, 2011.

M. Dou, L. Guan, J. Frahm, and H. Fuchs, Exploring High-Level Plane Primitives for Indoor 3D Reconstruction with a Hand-held RGB-D Camera, ACCV 2012 Workshops, pp.94-108, 2013.
DOI : 10.1007/978-3-642-37484-5_9

D. Dwibedi, T. Malisiewicz, V. Badrinarayanan, and A. Rabinovich, Deep cuboid detection, 2016.

M. A. Fischler and R. C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, vol.24, issue.6, pp.381-395, 1981.
DOI : 10.1145/358669.358692

V. Hedau, D. Hoiem, and D. Forsyth, Recovering the spatial layout of cluttered rooms, 2009 IEEE 12th International Conference on Computer Vision, pp.1849-1856, 2009.
DOI : 10.1109/ICCV.2009.5459411

S. Izadi, D. Kim, O. Hilliges, D. Molyneaux, R. Newcombe et al., KinectFusion, Proceedings of the 24th annual ACM symposium on User interface software and technology, UIST '11, pp.559-568, 2011.
DOI : 10.1145/2047196.2047270

Z. Jia, A. Gallagher, A. Saxena, C. , and T. , 3D-Based Reasoning with Blocks, Support, and Stability, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.8
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.307.3382

H. Jiang, Finding Approximate Convex Shapes in RGBD Images, pp.582-596, 2014.
DOI : 10.1007/978-3-319-10578-9_38

H. Jiang and J. Xiao, A Linear Approach to Matching Cuboids in RGBD Images, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.282

D. Lin, S. Fidler, and R. Urtasun, Holistic Scene Understanding for 3D Object Detection with RGBD Cameras, 2013 IEEE International Conference on Computer Vision, pp.1417-1424, 2013.
DOI : 10.1109/ICCV.2013.179

F. Mirzaei and S. Roumeliotis, Optimal estimation of vanishing points in a Manhattan world, 2011 International Conference on Computer Vision, pp.2454-2461, 2011.
DOI : 10.1109/ICCV.2011.6126530

D. Neumann, F. Lugauer, S. Bauer, J. Wasza, and J. Hornegger, Real-time RGB-D mapping and 3-D modeling on the GPU using the random ball cover data structure, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp.1161-1167, 2011.
DOI : 10.1109/ICCVW.2011.6130381

N. Neverova, D. Muselet, and A. Trémeau, 21/2 D Scene Reconstruction of Indoor Scenes from Single RGB-D Images, CCIW, pp.281-295, 2013.
DOI : 10.1007/978-3-642-36700-7_22

L. Piegl, On NURBS: a survey, IEEE Computer Graphics and Applications, vol.11, issue.1, pp.55-71, 1991.
DOI : 10.1109/38.67702

Z. Ren and E. B. Sudderth, Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.169

A. Richtsfeld, T. Mörwald, J. Prankl, J. Balzer, M. Zillich et al., Towards scene understanding -object segmentation using rgbd-images, CVWW, 2012.

A. Schwing and R. Urtasun, Efficient Exact Inference for 3D Indoor Scene Understanding, ECCV, pp.299-313, 2012.
DOI : 10.1007/978-3-642-33783-3_22
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.296.9178

N. Silberman, D. Hoiem, P. Kohli, F. , and R. , Indoor Segmentation and Support Inference from RGBD Images, ECCV, pp.746-760, 2012.
DOI : 10.1007/978-3-642-33715-4_54

C. Taylor and A. Cowley, Fast scene analysis using image and range data, 2011 IEEE International Conference on Robotics and Automation, pp.3562-3567, 2011.
DOI : 10.1109/ICRA.2011.5980326
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.5238

C. Taylor and A. Cowley, Parsing Indoor Scenes Using RGB-D Imagery, Robotics: Science and Systems VIII, 2012.
DOI : 10.15607/RSS.2012.VIII.051
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.6997

J. Zhang, C. Kan, A. G. Schwing, and R. Urtasun, Estimating the 3D Layout of Indoor Scenes and Its Clutter from Depth Sensors, 2013 IEEE International Conference on Computer Vision, pp.1273-1280, 2013.
DOI : 10.1109/ICCV.2013.161

Y. Zhang, S. Song, P. Tan, X. , and J. , PanoContext: A Whole-Room 3D Context Model for Panoramic Scene Understanding, pp.668-686, 2014.
DOI : 10.1007/978-3-319-10599-4_43