“Semantically supervised appearance decomposition for virtual staging from a single panorama” by Zhi, Chen, Boyadzhiev, Kang, Hebert, et al. …

We describe a novel approach to decompose a single panorama of an empty indoor environment into four appearance components: specular, direct sunlight, diffuse and diffuse ambient without direct sunlight. Our system is weakly supervised by automatically generated semantic maps (with floor, wall, ceiling, lamp, window and door labels) that have shown success on perspective views and are trained for panoramas using transfer learning without any further annotations. A GAN-based approach supervised by coarse information obtained from the semantic map extracts specular reflection and direct sunlight regions on the floor and walls. These lighting effects are removed via a similar GAN-based approach and a semantic-aware inpainting step. The appearance decomposition enables multiple applications including sun direction estimation, virtual furniture insertion, floor material replacement, and sun direction change, providing an effective tool for virtual home staging. We demonstrate the effectiveness of our approach on a large and recently released dataset of panoramas of empty homes.

1. Miika Aittala, Tim Weyrich, and Jaakko Lehtinen. 2015. Two-shot SVBRDF Capture for Stationary Materials. ACM Trans. Graph. (Proc. SIGGRAPH) 34, 4, Article 110 (July 2015), 13 pages. Google ScholarDigital Library
2. Yasushi Akashi and Takayuki Okatani. 2016. Separation of reflection components by sparse non-negative matrix factorization. Computer Vision and Image Understanding 100, 146 (2016), 77–85.Google ScholarDigital Library
3. Nikolaos Arvanitopoulos, Radhakrishna Achanta, and Sabine Susstrunk. 2017. Single image reflection suppression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4498–4506.Google ScholarCross Ref
4. Jonathan Barron and Jitendra Malik. 2015. Shape, Illumination, and Reflectance from Shading. IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (08 2015), 1670–1687. Google ScholarDigital Library
5. Anil S Baslamisli, Partha Das, Hoang-An Le, Sezer Karaoglu, and Theo Gevers. 2021. ShadingNet: image intrinsics by fine-grained shading decomposition. International Journal of Computer Vision (2021), 1–29.Google ScholarDigital Library
6. Sean Bell, Kavita Bala, and Noah Snavely. 2014. Intrinsic images in the wild. ACM Transactions on Graphics (TOG) 33, 4 (2014), 1–12.Google ScholarDigital Library
7. Mark Boss, Raphael Braun, Varun Jampani, Jonathan T Barron, Ce Liu, and Hendrik Lensch. 2021a. Nerd: Neural reflectance decomposition from image collections. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 12684–12694.Google ScholarCross Ref
8. Mark Boss, Varun Jampani, Raphael Braun, Ce Liu, Jonathan Barron, and Hendrik Lensch. 2021b. Neural-pil: Neural pre-integrated lighting for reflectance decomposition. Advances in Neural Information Processing Systems 34 (2021).Google Scholar
9. Mark Boss, Varun Jampani, Kihwan Kim, Hendrik Lensch, and Jan Kautz. 2020. Two-shot spatially-varying brdf and shape estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3982–3991.Google ScholarCross Ref
10. Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. 2017. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 4 (2017), 834–848.Google ScholarCross Ref
11. Zhe Chen, Shohei Nobuhara, and Ko Nishino. 2021. Invertible neural BRDF for object inverse rendering. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).Google ScholarCross Ref
12. Steve Cruz, Will Hutchcroft, Yuguang Li, Naji Khosravan, Ivaylo Boyadzhiev, and Sing Bing Kang. 2021. Zillow Indoor Dataset: Annotated Floor Plans With 360° Panoramas and 3D Room Layouts. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google ScholarCross Ref
13. Valentin Deschaintre, Miika Aittala, Fredo Durand, George Drettakis, and Adrien Bousseau. 2018. Single-image svbrdf capture with a rendering-aware deep network. ACM Transactions on Graphics (TOG) 37, 4 (2018), 1–15.Google ScholarDigital Library
14. Valentin Deschaintre, Miika Aittala, Frédo Durand, George Drettakis, and Adrien Bousseau. 2019. Flexible svbrdf capture with a multi-image deep network. In Computer graphics forum, Vol. 38. Wiley Online Library, 1–13.Google Scholar
15. Zheng Dong, Ke Xu, Yin Yang, Hujun Bao, Weiwei Xu, and Rynson WH Lau. 2021. Location-aware single image reflection removal. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 5017–5026.Google ScholarCross Ref
16. Gabriel Eilertsen, Joel Kronander, Gyorgy Denes, Rafał K Mantiuk, and Jonas Unger. 2017. HDR image reconstruction from a single exposure using deep CNNs. ACM Transactions on Graphics (TOG) 36, 6 (2017), 1–15.Google ScholarDigital Library
17. Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, and David Wipf. 2017. A generic deep architecture for single image reflection removal and image smoothing. In Proceedings of the IEEE International Conference on Computer Vision. 3238–3247.Google ScholarCross Ref
18. Gang Fu, Qing Zhang, Lei Zhu, Ping Li, and Chunxia Xiao. 2021. A Multi-Task Network for Joint Specular Highlight Detection and Removal. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7752–7761.Google ScholarCross Ref
19. Duan Gao, Xiao Li, Yue Dong, Pieter Peers, Kun Xu, and Xin Tong. 2019. Deep inverse rendering for high-resolution SVBRDF estimation from an arbitrary number of images. ACM Trans. Graph. 38, 4 (2019), 134–1.Google ScholarDigital Library
20. Marc-André Gardner, Yannick Hold-Geoffroy, Kalyan Sunkavalli, Christian Gagné, and Jean-Francois Lalonde. 2019. Deep parametric indoor lighting estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 7175–7183.Google ScholarCross Ref
21. Marc-André Gardner, Kalyan Sunkavalli, Ersin Yumer, Xiaohui Shen, Emiliano Gambaretto, Christian Gagné, and Jean-François Lalonde. 2017. Learning to predict indoor illumination from a single image. ACM Transactions on Graphics (TOG) 36, 6 (2017), 1–14.Google ScholarDigital Library
22. Mathieu Garon, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, and Jean-François Lalonde. 2019. Fast spatially-varying indoor lighting estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6908–6917.Google ScholarCross Ref
23. Stamatios Georgoulis, Konstantinos Rematas, Tobias Ritschel, Efstratios Gavves, Mario Fritz, Luc Van Gool, and Tinne Tuytelaars. 2017. Reflectance and natural illumination from single-material specular objects using deep learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 8 (2017), 1932–1947.Google ScholarCross Ref
24. Vasileios Gkitsas, Nikolaos Zioulis, Federico Alvarez, Dimitrios Zarpalas, and Petros Daras. 2020. Deep lighting environment map estimation from spherical panoramas. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 640–641.Google ScholarCross Ref
25. Roger Grosse, Micah K Johnson, Edward H Adelson, and William T Freeman. 2009. Ground truth dataset and baseline evaluations for intrinsic image algorithms. In 2009 IEEE 12th International Conference on Computer Vision. IEEE, 2335–2342.Google ScholarCross Ref
26. Jie Guo, Zuojian Zhou, and Limin Wang. 2018. Single image highlight removal with a sparse and low-rank reflection model. In Proceedings of the European Conference on Computer Vision (ECCV). 268–283.Google ScholarDigital Library
27. Yannick Hold-Geoffroy, Akshaya Athawale, and Jean-François Lalonde. 2019. Deep sky modeling for single image outdoor lighting estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6927–6935.Google ScholarCross Ref
28. Yannick Hold-Geoffroy, Kalyan Sunkavalli, Sunil Hadap, Emiliano Gambaretto, and Jean-François Lalonde. 2017. Deep outdoor illumination estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7312–7321.Google ScholarCross Ref
29. Yuchen Hong, Qian Zheng, Lingran Zhao, Xudong Jiang, Alex C Kot, and Boxin Shi. 2021. Panoramic Image Reflection Removal. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7762–7771.Google ScholarCross Ref
30. Yiwei Hu, Chengan He, Valentin Deschaintre, Julie Dorsey, and Holly Rushmeier. 2022. An Inverse Procedural Modeling Pipeline for SVBRDF Maps. ACM Transactions on Graphics (TOG) 41, 2 (2022), 1–17.Google ScholarDigital Library
31. Wenzel Jakob. 2010. Mitsuba renderer. http://www.mitsuba-renderer.org.Google Scholar
32. Michael Janner, Jiajun Wu, Tejas D Kulkarni, Ilker Yildirim, and Joshua B Tenenbaum. 2017. Self-supervised intrinsic image decomposition. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 5938–5948.Google Scholar
33. Salma Jiddi, Philippe Robert, and Eric Marchand. 2020. Detecting specular reflections and cast shadows to estimate reflectance and illumination of dynamic indoor scenes. IEEE Transactions on Visualization and Computer Graphics (2020).Google Scholar
34. Justin Johnson, Alexandre Alahi, and Li Fei-Fei. 2016. Perceptual losses for real-time style transfer and super-resolution. In European Conference on Computer Vision. Springer, 694–711.Google ScholarCross Ref
35. Kevin Karsch, Varsha Hedau, David Forsyth, and Derek Hoiem. 2011. Rendering Synthetic Objects into Legacy Photographs. ACM Transactions on Computer Systems 30, 6 (1 Dec. 2011), 1–12. Copyright: Copyright 2017 Elsevier B.V., All rights reserved. Google ScholarDigital Library
36. Kevin Karsch, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, Hailin Jin, Rafael Fonte, Michael Sittig, and David Forsyth. 2014. Automatic scene inference for 3d object compositing. ACM Transactions on Graphics (TOG) 33, 3 (2014), 1–15.Google ScholarDigital Library
37. Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, and Luc Van Gool. 2021. Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3804–3814.Google ScholarCross Ref
38. Hyeongwoo Kim, Hailin Jin, Sunil Hadap, and Inso Kweon. 2013. Specular reflection separation using dark channel prior. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1460–1467.Google ScholarDigital Library
39. Kihwan Kim, Jinwei Gu, S. Tyree, P. Molchanov, M. Nießner, and J. Kautz. 2017. A Lightweight Approach for On-the-Fly Reflectance Estimation. 2017 IEEE International Conference on Computer Vision (ICCV) (2017), 20–28.Google ScholarCross Ref
40. Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR.Google Scholar
41. Jean-François Lalonde, Alexei A Efros, and Srinivasa G Narasimhan. 2012. Estimating the natural illumination conditions from a single outdoor image. International Journal of Computer Vision 98, 2 (2012), 123–145.Google ScholarDigital Library
42. Jean-François Lalonde, Srinivasa G Narasimhan, and Alexei A Efros. 2010. What do the sun and the sky tell us about the camera? International Journal of Computer Vision 88, 1 (2010), 24–51.Google ScholarDigital Library
43. John Lambert, Zhuang Liu, Ozan Sener, James Hays, and Vladlen Koltun. 2020. MSeg: A Composite Dataset for Multi-domain Semantic Segmentation. In Computer Vision and Pattern Recognition (CVPR).Google Scholar
44. Anat Levin and Yair Weiss. 2007. User assisted separation of reflections from a single image using a sparsity prior. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 9 (2007), 1647–1654.Google ScholarDigital Library
45. Chao Li, Yixiao Yang, Kun He, Stephen Lin, and John E Hopcroft. 2020b. Single image reflection removal through cascaded refinement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3565–3574.Google ScholarCross Ref
46. Junxuan Li, Hongdong Li, and Yasuyuki Matsushita. 2021a. Lighting, Reflectance and Geometry Estimation From 360deg Panoramic Stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10591–10600.Google Scholar
47. Yu Li and Michael S Brown. 2014. Single image layer separation using relative smoothness. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2752–2759.Google ScholarDigital Library
48. Zhengqin Li, Mohammad Shafiei, Ravi Ramamoorthi, Kalyan Sunkavalli, and Manmohan Chandraker. 2020a. Inverse rendering for complex indoor scenes: Shape, spatially-varying lighting and SVBRDF from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2475–2484.Google ScholarCross Ref
49. Zhengqi Li and Noah Snavely. 2018a. Cgintrinsics: Better intrinsic image decomposition through physically-based rendering. In Proceedings of the European Conference on Computer Vision (ECCV). 371–387.Google ScholarDigital Library
50. Zhengqi Li and Noah Snavely. 2018b. Learning intrinsic image decomposition from watching the world. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 9039–9048.Google ScholarCross Ref
51. Zhengqin Li, Kalyan Sunkavalli, and Manmohan Chandraker. 2018. Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image. In Proceedings of the European Conference on Computer Vision (ECCV).Google ScholarDigital Library
52. Zhengqin Li, Ting-Wei Yu, Shen Sang, Sarah Wang, Sai Bi, Zexiang Xu, Hong-Xing Yu, Kalyan Sunkavalli, Miloš Hašan, Ravi Ramamoorthi, and Manmohan Chandraker. 2021b. OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets., 7190–7199 pages.Google Scholar
53. John Lin, Mohamed El Amine Seddik, Mohamed Tamaazousti, Youssef Tamaazousti, and Adrien Bartoli. 2019. Deep multi-class adversarial specularity removal. In Scandinavian Conference on Image Analysis. Springer, 3–15.Google ScholarDigital Library
54. Yang Liu, Theo Gevers, and Xueqing Li. 2014. Estimation of sunlight direction using 3D object models. IEEE Transactions on Image Processing 24, 3 (2014), 932–942.Google Scholar
55. Yunfei Liu, Yu Li, Shaodi You, and Feng Lu. 2020. Unsupervised learning for intrinsic image decomposition from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3248–3257.Google ScholarCross Ref
56. Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3431–3440.Google ScholarCross Ref
57. Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. 2020. Nerf: Representing scenes as neural radiance fields for view synthesis. In European conference on computer vision. Springer, 405–421.Google ScholarDigital Library
58. Evangelos Ntavelis, Andrés Romero, Iason Kastanis, Luc Van Gool, and Radu Timofte. 2020. SESAME: semantic editing of scenes by adding, manipulating or erasing objects. In European Conference on Computer Vision. Springer, 394–411.Google ScholarDigital Library
59. Jeong Joon Park, Aleksander Holynski, and Steven M Seitz. 2020. Seeing the World in a Bag of Chips. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1417–1427.Google ScholarCross Ref
60. Giovanni Pintore, Marco Agus, and Enrico Gobbetti. 2020. AtlantaNet: Inferring the 3D Indoor Layout from a Single 360 Image Beyond the Manhattan World Assumption. In European Conference on Computer Vision. Springer, 432–448.Google ScholarDigital Library
61. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, 234–241.Google ScholarCross Ref
62. S. Sengupta, Jinwei Gu, Kihwan Kim, Guilin Liu, D. Jacobs, and J. Kautz. 2019. Neural Inverse Rendering of an Indoor Scene From a Single Image. 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (2019), 8597–8606.Google ScholarCross Ref
63. S. Sengupta, A. Kanazawa, Carlos D. Castillo, and D. Jacobs. 2018. SfSNet: Learning Shape, Reflectance and Illuminance of Faces ‘in the Wild’. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018), 6296–6305.Google ScholarCross Ref
64. Steven A Shafer. 1985. Using color to separate reflection components. Color Research & Application 10, 4 (1985), 210–218.Google ScholarDigital Library
65. Hui-Liang Shen and Zhi-Huan Zheng. 2013. Real-time highlight removal using intensity ratio. Applied optics 52, 19 (2013), 4483–4493.Google Scholar
66. Jian Shi, Yue Dong, Hao Su, and Stella X Yu. 2017. Learning non-lambertian object intrinsics across shapenet categories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1685–1694.Google ScholarCross Ref
67. YiChang Shih, Dilip Krishnan, Fredo Durand, and William T Freeman. 2015. Reflection removal using ghosting cues. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3193–3201.Google Scholar
68. Z. Shu, Ersin Yumer, Sunil Hadap, Kalyan Sunkavalli, E. Shechtman, and D. Samaras. 2017. Neural Face Editing with Intrinsic Image Disentangling. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017), 5444–5453.Google Scholar
69. Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In ICLR.Google Scholar
70. Gowri Somanath and Daniel Kurz. 2020. HDR Environment Map Estimation for Real-Time Augmented Reality. arXiv preprint arXiv:2011.10687 (2020).Google Scholar
71. Pratul P Srinivasan, Ben Mildenhall, Matthew Tancik, Jonathan T Barron, Richard Tucker, and Noah Snavely. 2020. Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8080–8089.Google ScholarCross Ref
72. Yu-Chuan Su and Kristen Grauman. 2017. Learning spherical convolution for fast features from 360 imagery. Advances in Neural Information Processing Systems 30 (2017), 529–539.Google Scholar
73. Yu-Chuan Su and Kristen Grauman. 2019. Kernel transformer networks for compact spherical convolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9442–9451.Google ScholarCross Ref
74. Cheng Sun, Chi-Wei Hsiao, Min Sun, and Hwann-Tzong Chen. 2019. Horizonnet: Learning room layout with 1d representation and pano stretch data augmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1047–1056.Google ScholarCross Ref
75. Tristan Swedish, Connor Henley, and Ramesh Raskar. 2021. Objects As Cameras: Estimating High-Frequency Illumination From Shadows. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 2593–2602.Google ScholarCross Ref
76. Robby T Tan and Katsushi Ikeuchi. 2005. Separating Reflection Components of Textured Surfaces Using a Single Image. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 2 (2005), 178–193.Google ScholarDigital Library
77. Marshall Tappen, William Freeman, and Edward Adelson. 2003. Recovering Intrinsic Images from a Single Image. In Advances in Neural Information Processing Systems, S. Becker, S. Thrun, and K. Obermayer (Eds.), Vol. 15. MIT Press. https://proceedings.neurips.cc/paper/2002/file/fa2431bf9d65058fe34e9713e32d60e6-Paper.pdfGoogle Scholar
78. Tatsumi Uezato, Danfeng Hong, Naoto Yokoya, and Wei He. 2020. Guided deep decoder: Unsupervised image pair fusion. In European Conference on Computer Vision. Springer, 87–102.Google ScholarDigital Library
79. Renjie Wan, Boxin Shi, Ling-Yu Duan, Ah-Hwee Tan, and Alex C Kot. 2018. Crrn: Multi-scale guided concurrent reflection removal network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4777–4785.Google ScholarCross Ref
80. Renjie Wan, Boxin Shi, Tan Ah Hwee, and Alex C Kot. 2016. Depth of field guided reflection removal. In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 21–25.Google ScholarCross Ref
81. Fu-En Wang, Yu-Hsuan Yeh, Min Sun, Wei-Chen Chiu, and Yi-Hsuan Tsai. 2021c. LED2-Net: Monocular 360deg Layout Estimation via Differentiable Depth Rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 12956–12965.Google ScholarCross Ref
82. Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al. 2020. Deep high-resolution representation learning for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).Google Scholar
83. Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan. 2021b. Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 1905–1914.Google ScholarCross Ref
84. Zian Wang, Jonah Philion, Sanja Fidler, and Jan Kautz. 2021a. Learning indoor inverse rendering with 3d spatially-varying lighting. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 12538–12547.Google ScholarCross Ref
85. Henrique Weber, Donald Prévost, and Jean-François Lalonde. 2018. Learning to estimate indoor lighting from 3D objects. In 2018 International Conference on 3D Vision (3DV). IEEE, 199–207.Google ScholarCross Ref
86. Kaixuan Wei, Jiaolong Yang, Ying Fu, David Wipf, and Hua Huang. 2019. Single image reflection removal exploiting misaligned training data and network enhancements. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8178–8187.Google ScholarCross Ref
87. Zhongqi Wu, Chuanqing Zhuang, Jian Shi, Jun Xiao, and Jianwei Guo. 2020. Deep Specular Highlight Removal for Single Real-world Image. In SIGGRAPH Asia 2020 Posters. 1–2.Google Scholar
88. Qingxiong Yang, Shengnan Wang, and Narendra Ahuja. 2010. Real-time specular highlight removal using bilateral filtering. In European Conference on Computer Vision. Springer, 87–100.Google ScholarCross Ref
89. Yizhou Yu, Paul Debevec, Jitendra Malik, and Tim Hawkins. 1999. Inverse global illumination: Recovering reflectance models of real scenes from photographs. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques. 215–224.Google ScholarDigital Library
90. Edward Zhang, Michael F Cohen, and Brian Curless. 2016. Emptying, refurnishing, and relighting indoor spaces. ACM Transactions on Graphics (TOG) 35, 6 (2016), 1–14.Google ScholarDigital Library
91. Jinsong Zhang, Kalyan Sunkavalli, Yannick Hold-Geoffroy, Sunil Hadap, Jonathan Eisenman, and Jean-François Lalonde. 2019. All-weather deep outdoor lighting estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10158–10166.Google ScholarCross Ref
92. Kai Zhang, Fujun Luan, Qianqian Wang, Kavita Bala, and Noah Snavely. 2021. PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5453–5462.Google ScholarCross Ref
93. Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. 2018a. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition. 586–595.Google ScholarCross Ref
94. Xuaner Zhang, Ren Ng, and Qifeng Chen. 2018b. Single image reflection separation with perceptual losses. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4786–4794.Google ScholarCross Ref
95. Jia Zheng, Junfei Zhang, Jing Li, Rui Tang, Shenghua Gao, and Zihan Zhou. 2020. Structured3d: A large photo-realistic dataset for structured 3d modeling. In European Conference on Computer Vision. Springer, 519–535.Google ScholarDigital Library
96. Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. 2017. Scene parsing through ade20k dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition. 633–641.Google ScholarCross Ref
97. Chuhang Zou, Alex Colburn, Qi Shan, and Derek Hoiem. 2018. Layoutnet: Reconstructing the 3D room layout from a single rgb image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2051–2059.Google ScholarCross Ref