Deep learning model to reconstruct 3D cityscapes by generating depth maps from omnidirectional images and its application to visual preference prediction

[1]  Mei Wang,et al.  Deep Visual Domain Adaptation: A Survey , 2018, Neurocomputing.

[2]  Žiga Kokalj,et al.  Application of sky-view factor for the visualisation of historic landscape features in lidar-derived relief models , 2011, Antiquity.

[3]  Takayuki Okatani,et al.  Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps With Accurate Object Boundaries , 2018, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[4]  Matthias Nießner,et al.  Spherical CNNs on Unstructured Grids , 2019, ICLR.

[5]  Enrico Fabrizio,et al.  Visibility analysis in urban spaces: a raster-based approach and case studies , 2015 .

[6]  D. Chang,et al.  Exploration of Isovist Fields to Model 3D Visibility With Building Facade , 2011 .

[7]  Tobias Preis,et al.  Using deep learning to quantify the beauty of outdoor places , 2017, Royal Society Open Science.

[8]  Petros Daras,et al.  OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas , 2018, ECCV.

[9]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[10]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[11]  S. Law,et al.  Take a Look Around: Using Street View and Satellite Images to Estimate House Prices , 2019 .

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Atsushi and Furuta Airi Takizawa,et al.  3D Spatial Analysis Method with First-Person Viewpoint by Deep Convolutional Neural Network with Omnidirectional RGB and Depth Images , 2017, Proceedings of the 35th International Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe) [Volume 2].

[15]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[17]  Andreas Geiger,et al.  SphereNet: Learning Spherical Representations for Detection and Classification in Omnidirectional Images , 2018, ECCV.

[18]  M. Benedikt,et al.  To Take Hold of Space: Isovists and Isovist Fields , 1979 .

[19]  Nick Hedley,et al.  Unpacking isovists: a framework for 3D spatial visibility analysis , 2016 .

[20]  Thierry Joliveau,et al.  A New Algorithm for 3D Isovists , 2013 .

[21]  Yao Shen,et al.  Street-Frontage-Net: urban image classification using deep convolutional neural networks , 2018, Int. J. Geogr. Inf. Sci..

[22]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[23]  Yao Yao,et al.  Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China , 2019, Environment international.

[24]  Max Welling,et al.  Spherical CNNs , 2018, ICLR.

[25]  Youngchul Kim,et al.  A new 3D space syntax metric based on 3D isovist capture in urban space using remote sensing technology , 2019, Comput. Environ. Urban Syst..

[26]  Jiale Wang,et al.  A human-machine adversarial scoring framework for urban perception assessment using street-view images , 2019, Int. J. Geogr. Inf. Sci..

[27]  Weixing Zhang,et al.  Urban Forestry & Urban Greening , 2015 .

[28]  Michael Batty,et al.  Exploring Isovist Fields: Space and Shape in Architectural and Urban Morphology , 2001 .

[29]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[30]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[31]  Rares Ambrus,et al.  SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[32]  Jonathan Krause,et al.  Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States , 2017, Proceedings of the National Academy of Sciences.

[33]  Thierry Joliveau,et al.  A New Algorithm for 3D Isovist , 2012 .

[34]  Hui Wang,et al.  A machine learning-based method for the large-scale evaluation of the qualities of the urban environment , 2017, Comput. Environ. Urban Syst..

[35]  Paolo Valigi,et al.  Fast robust monocular depth estimation for Obstacle Detection with fully convolutional networks , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[36]  Ashton M. Shortridge,et al.  Systematic review of the use of Google Street View in health research: Major themes, strengths, weaknesses and possibilities for future research☆ , 2018, Health & place.

[37]  Ning Qian,et al.  On the momentum term in gradient descent learning algorithms , 1999, Neural Networks.

[38]  Antonio M. López,et al.  The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Nassir Navab,et al.  Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images , 2018, ECCV.

[40]  George Papandreou,et al.  Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.