Abstract
IndoorNet is an approach to generate 3D layouts for indoor scenes. By employing a fully convolutional network (FCN), it predicts the layout of the room using only a single 360 degree panorama image. The FCN uses geometrical features, i.e., vanishing points along with the panoramic image for better contextual understanding. Further, we optimize the boundary map and the corner map to improve their accuracy. We then use these improved maps to generate the 3D layout of the room. As compared to the state of the art, our network architecture is faster and lighter, with improved results.
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References
Mirowski P, Pascanu R, Viola F, Soyer H, Ballard AJ, Banino A, Denil M, Goroshin R, Sifre L, Kavukcuoglu K, Kumaran D, Learning to navigate in complex environments. arXiv preprint arXiv:1611.03673
Savva M, Chang A, Dosovitskiy A, Funkhouser T, Koltun V (Dec 2017) MINOS: multimodal indoor simulator for navigation in complex environments. arXiv:1712.03931 [cs]
Xiao J, Furukawa Y (2014) Reconstructing the world’s museums. Int J Comput Vis 110(3):243–258
Tutenel T, Bidarra R, Smelik R, De Kraker K (2009) Rule-based layout solving and its application to procedural interior generation. In: CASA workshop on 3D advanced media in gaming and simulation
Coughlan J, Yuille A (1999) Manhattan World: compass direction from a single image by Bayesian inference. In: Proceedings of the seventh IEEE international conference on computer vision
Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495
Chen L, Papandreou G, Kokkinos I, Murphy K, Yuille A (2018) DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848
Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Computer science medical image computing and computer-assisted intervention—MICCAI, pp 234–241
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: IEEE conference on computer vision and pattern recognition
Mallya A, Lazebnik S (2015) Learning informative edge maps for indoor scene layout prediction. In: 2015 IEEE international conference on computer vision (ICCV), Santiago, Chile, pp 936–944
Dasgupta S, Fang K, Chen K, Savarese S (2016) DeLay: robust spatial layout estimation for cluttered indoor scenes. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA
Lee C, Badrinarayanan V, Malisiewicz T, Rabinovich A (2017) RoomNet: end-to-end room layout estimation. In: 2017 IEEE international conference on computer vision (ICCV), Venice
Zhang Y, Song S, Tan P, Xiao J (2014) PanoContext: a whole-room 3D context model for panoramic scene understanding. In: Computer vision ECCV 2014 lecture notes in computer science, pp 668–686
Zou C, Colburn A, Shan Q, Hoiem D (2018) LayoutNet: reconstructing the 3D room layout from a single RGB image. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT
Fernandez-Labrador C, Perez-Yus A, Lopez-Nicolas G, Guerrero J (2018) Layouts from panoramic images with geometry and deep learning. IEEE Robot Autom Lett 3(4):3153–3160
Fernandez-Labrador C, Facil J, Perez-Yus A, Demonceaux C, Guerrero J (2018) PanoRoom: from the sphere to the 3D layout. arXiv:1808.09879[cs]
Zhao H, Lu M, Yao A, Guo Y, Chen Y, Zhang L (2017) Physics inspired optimization on semantic transfer features: an alternative method for room layout estimation. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI
Armeni I, Sax S, Zamir A, Savarese S (2017) Joint 2D-3D-semantic data for indoor scene understanding. arXiv:1702.01105
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Kotadia, Y., Mehta, K., Manjrekar, M., Karani, R. (2020). IndoorNet: Generating Indoor Layouts from a Single Panorama Image. In: Vasudevan, H., Michalas, A., Shekokar, N., Narvekar, M. (eds) Advanced Computing Technologies and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3242-9_6
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DOI: https://doi.org/10.1007/978-981-15-3242-9_6
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