{"type":"ScholarlyArticle","title":"2D Gaussian Splatting for Outdoor Scene Decomposition and Relighting","authors":[{"name":"Wei Feng","slug":"wei-feng","given":"Wei","family":"Feng","affiliation":"Tianjin University"},{"name":"Kangrui Ye","slug":"kangrui-ye","given":"Kangrui","family":"Ye","affiliation":"Tianjin University"},{"name":"Qi Zhang","slug":"qi-zhang","given":"Qi","family":"Zhang","affiliation":"Tianjin University"},{"name":"Qian Zhang","slug":"qian-zhang","given":"Qian","family":"Zhang","affiliation":"Tianjin University"},{"name":"Nan Li","slug":"nan-li","given":"Nan","family":"Li","affiliation":"Tianjin University"}],"abstract":"Gaussian splatting techniques have recently revolutionized outdoor scene decomposition and relighting through multi-view images. However, achieving high rendering quality still requires a fixed lighting condition among all input views, which is costly or even impractical to capture in outdoor scenes. In this paper, we propose outdoor scene decomposition and relighting with 2D Gaussian splatting (OSDR-GS), a novel inverse rendering strategy under outdoor changing and unknown lighting conditions. Firstly, we present a lighting-based group learning framework that categorizes input images into multiple lighting groups, to learn the separate lighting from each group individually. Secondly, OSDR-GS introduces a fine-grained outdoor lighting component to represent sun-light and sky-light, respectively, which are also adjusted via the correlative exposure factors adaptively. Finally, we construct a visibility-driven shadow module to characterize the nuanced interplay of light and occlusion realistically, for eliminating the uncertainty of dark pixels on lighting-based group learning. Extensive experiments on multiple challenging outdoor datasets validate the effectiveness of OSDR-GS, which achieves the state-of-the-art performance in changing lighting scene inverse rendering.","doi":"10.24963/ijcai.2025/111","url":"https://ijcai.org/proceedings/2025/111","pdf":"https://ijcai.org/proceedings/2025/0111.pdf","markdown":"https://ijcai.org/proceedings/2025/111.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/111","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":111,"pages":{"first":990,"last":998},"keywords":["Computer Vision: CV: 3D computer vision","Computer Vision: CV: Computational photography","Computer Vision: CV: Image and video synthesis and generation","Computer Vision: CV: Scene analysis and understanding"],"videos":[],"references":[{"position":1,"raw":"[Barron et al., 2021] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale represen- tation for anti-aliasing neural radiance fields. In Proceed- ings of the IEEE/CVF international conference on com- puter vision, pages 5855-5864, 2021.","title":"Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields","authors":"Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, Pratul P Srinivasan","year":"2021","venue":"Proceedings of the IEEE/CVF international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Barron et al., 2022] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip- nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5470-5479, 2022.","title":"Mipnerf 360: Unbounded anti-aliased neural radiance fields","authors":"Jonathan T Barron, Ben Mildenhall, Dor Verbin, Peter Pratul P Srinivasan, Hedman","year":"2022","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bhattacharyya, 1943] Anil Bhattacharyya. On a measure of divergence between two statistical populations defined by their probability distribution. Bulletin of the Calcutta Mathematical Society, 35:99-110, 1943.","title":"On a measure of divergence between two statistical populations defined by their probability distribution","authors":"Anil Bhattacharyya","year":"1943","venue":"Bulletin of the Calcutta Mathematical Society","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bi et al., 2020] Sai Bi, Zexiang Xu, Pratul Srinivasan, Ben Mildenhall, Kalyan Sunkavalli, Miloš Hašan, Yannick Hold-Geoffroy, David Kriegman, and Ravi Ramamoorthi. Neural reflectance fields for appearance acquisition. arXiv preprint arXiv:2008.03824, 2020.","title":"Neural reflectance fields for appearance acquisition","authors":"Sai Bi, Zexiang Xu, Pratul Srinivasan, Ben Mildenhall, Kalyan Sunkavalli, Miloš Hašan, Yannick Hold-Geoffroy, David Kriegman, Ravi Ramamoorthi","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bi et al., 2024] Zoubin Bi, Yixin Zeng, Chong Zeng, Fan Pei, Xiang Feng, Kun Zhou, and Hongzhi Wu. Gs3: Ef- ficient relighting with triple gaussian splatting. In SIG- GRAPH Asia 2024 Conference Papers, pages 1-12, 2024.","title":"Gs3: Efficient relighting with triple gaussian splatting","authors":"Zoubin Bi, Yixin Zeng, Chong Zeng, Fan Pei, Xiang Feng, Kun Zhou, Hongzhi Wu","year":"2024","venue":"SIG-GRAPH Asia 2024 Conference Papers","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Bian et al., 2023] Wenjing Bian, Zirui Wang, Kejie Li, Jia- Wang Bian, and Victor Adrian Prisacariu. Nope-nerf: Op- timising neural radiance field with no pose prior. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4160-4169, 2023.","title":"Nope-nerf: Optimising neural radiance field with no pose prior","authors":"Wenjing Bian, Zirui Wang, Kejie Li, Jia-Wang Bian, Adrian Victor, Prisacariu","year":"2023","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Boss et al., 2021] Mark Boss, Raphael Braun, Varun Jam- pani, Jonathan T Barron, Ce Liu, and Hendrik Lensch. Nerd: Neural reflectance decomposition from image col- lections. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12684-12694, 2021.","title":"Nerd: Neural reflectance decomposition from image collections","authors":"Mark Boss, Raphael Braun, Varun Jampani, Jonathan T Barron, Ce Liu, Hendrik Lensch","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen and Wang, 2024] Guikun Chen and Wenguan Wang. A survey on 3d gaussian splatting. arXiv preprint arXiv:2401.03890, 2024.","title":"A survey on 3d gaussian splatting","authors":"Chen, Wang ;, Guikun Chen, Wenguan Wang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chen et al., 2022] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radi- ance fields. In European conference on computer vision, pages 333-350. Springer, 2022.","title":"Tensorf: Tensorial radiance fields","authors":"Chen","year":"2022","venue":"European conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Chen et al., 2024] Danpeng Chen, Hai Li, Weicai Ye, Yifan Wang, Weijian Xie, Shangjin Zhai, Nan Wang, Haomin Liu, Hujun Bao, and Guofeng Zhang. Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface re- construction. arXiv preprint arXiv:2406.06521, 2024.","title":"Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface reconstruction","authors":"Danpeng Chen, Hai Li, Weicai Ye, Yifan Wang, Weijian Xie, Shangjin Zhai, Nan Wang, Haomin Liu, Hujun Bao, Guofeng Zhang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Feng et al., 2015] Wei Feng, Fei-Peng Tian, Qian Zhang, Nan Zhang, Liang Wan, and Jizhou Sun. Fine-grained change detection of misaligned scenes with varied illumi- nations. In Proceedings of the IEEE International Confer- ence on Computer Vision, pages 1260-1268, 2015.","title":"Fine-grained change detection of misaligned scenes with varied illuminations","authors":"Wei Feng, Fei-Peng Tian, Qian Zhang, Nan Zhang, Liang Wan, Jizhou Sun","year":"2015","venue":"Proceedings of the IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gao et al., 2022] Kyle Gao, Yina Gao, Hongjie He, Dening Lu, Linlin Xu, and Jonathan Li. Nerf: Neural radiance field in 3d vision, a comprehensive review. arXiv preprint arXiv:2210.00379, 2022.","title":"Nerf: Neural radiance field in 3d vision, a comprehensive review","authors":"Kyle Gao, Yina Gao, Hongjie He, Dening Lu, Linlin Xu, Jonathan Li","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Gao et al., 2023] Jian Gao, Chun Gu, Youtian Lin, Hao Zhu, Xun Cao, Li Zhang, and Yao Yao. Relightable 3d gaussian: Real-time point cloud relighting with brdf de- composition and ray tracing. arXiv e-prints, pages arXiv- 2311, 2023.","title":"Relightable 3d gaussian: Real-time point cloud relighting with brdf decomposition and ray tracing","authors":"Jian Gao, Chun Gu, Youtian Lin, Hao Zhu, Xun Cao, Li Zhang, Yao Yao","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Gardner et al., 2024] James AD Gardner, Evgenii Kashin, Bernhard Egger, and William AP Smith. The sky's the limit: Relightable outdoor scenes via a sky-pixel con- strained illumination prior and outside-in visibility. In Eu- ropean Conference on Computer Vision, pages 126-143. Springer, 2024.","title":"The sky's the limit: Relightable outdoor scenes via a sky-pixel constrained illumination prior and outside-in visibility","authors":"Gardner","year":"2024","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Guo et al., 2024] Yijia Guo, Yuanxi Bai, Liwen Hu, Ziyi Guo, Mianzhi Liu, Yu Cai, Tiejun Huang, and Lei Ma. Prtgs: Precomputed radiance transfer of gaussian splats for real-time high-quality relighting. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 5112-5120, 2024.","title":"Prtgs: Precomputed radiance transfer of gaussian splats for real-time high-quality relighting","authors":"Yijia Guo, Yuanxi Bai, Liwen Hu, Ziyi Guo, Mianzhi Liu, Yu Cai, Tiejun Huang, Lei Ma","year":"2024","venue":"Proceedings of the 32nd ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Huang et al., 2024] Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically accurate radiance fields. In ACM SIG- GRAPH 2024 conference papers, pages 1-11, 2024.","title":"2d gaussian splatting for geometrically accurate radiance fields","authors":"Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, Shenghua Gao","year":"2024","venue":"ACM SIG-GRAPH 2024 conference papers","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kajiya, 1986] James T Kajiya. The rendering equation. In Proceedings of the 13th annual conference on Computer graphics and interactive techniques, pages 143-150, 1986.","title":"The rendering equation","authors":"T James, Kajiya","year":"1986","venue":"Proceedings of the 13th annual conference on Computer graphics and interactive techniques","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kerbl et al., 2023] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaus- sian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139-1, 2023.","title":"3d gaussian splatting for real-time radiance field rendering","authors":"Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George Drettakis","year":"2023","venue":"ACM Trans. Graph","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2023] Zhong Li, Liangchen Song, Zhang Chen, Xiangyu Du, Lele Chen, Junsong Yuan, and Yi Xu. Relit- neulf: Efficient relighting and novel view synthesis via neural 4d light field. In Proceedings of the 31st ACM In- ternational Conference on Multimedia, pages 7007-7016, 2023.","title":"Relitneulf: Efficient relighting and novel view synthesis via neural 4d light field","authors":"Zhong Li, Liangchen Song, Zhang Chen, Xiangyu Du, Lele Chen, Junsong Yuan, Yi Xu","year":"2023","venue":"Proceedings of the 31st ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liang et al., 2024] Zhihao Liang, Qi Zhang, Ying Feng, Ying Shan, and Kui Jia. Gs-ir: 3d gaussian splatting for in- verse rendering. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 21644-21653, 2024.","title":"Gs-ir: 3d gaussian splatting for inverse rendering","authors":"Zhihao Liang, Qi Zhang, Ying Feng, Ying Shan, Kui Jia","year":"2024","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lin et al., 2021] Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey. Barf: Bundle-adjusting neural radiance fields. In Proceedings of the IEEE/CVF interna- tional conference on computer vision, pages 5741-5751, 2021.","title":"Barf: Bundle-adjusting neural radiance fields","authors":"Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, Simon Lucey","year":"2021","venue":"Proceedings of the IEEE/CVF international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Lin et al., 2024] Jiaqi Lin, Zhihao Li, Xiao Tang, Jianzhuang Liu, Shiyong Liu, Jiayue Liu, Yangdi Lu, Xiaofei Wu, Songcen Xu, Youliang Yan, et al. Vast- gaussian: Vast 3d gaussians for large scene reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5166-5175, 2024.","title":"Vastgaussian: Vast 3d gaussians for large scene reconstruction","authors":"Jiaqi Lin, Zhihao Li, Xiao Tang, Jianzhuang Liu, Shiyong Liu, Jiayue Liu, Yangdi Lu, Xiaofei Wu, Songcen Xu, Youliang Yan","year":"2024","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[MacQueen, 1967] James MacQueen. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathemat- ical statistics and probability, volume 1, pages 281-297.","title":"Some methods for classification and analysis of multivariate observations","authors":"James Macqueen","year":null,"venue":"Proceedings of the fifth Berkeley symposium on mathematical statistics and probability","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Mildenhall et al., 2021] Ben Mildenhall, Pratul P Srini- vasan, Matthew Tancik, Jonathan T Barron, Ravi Ra- mamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021.","title":"Nerf: Representing scenes as neural radiance fields for view synthesis","authors":"Ben Mildenhall, P Pratul, Matthew Srinivasan, Jonathan T Tancik, Ravi Barron, Ren Ramamoorthi, Ng","year":"2021","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Rudnev et al., 2022] Viktor Rudnev, Mohamed Elgharib, William Smith, Lingjie Liu, Vladislav Golyanik, and Christian Theobalt. Nerf for outdoor scene relighting. In European Conference on Computer Vision, pages 615- 631. Springer, 2022.","title":"Nerf for outdoor scene relighting","authors":"Mohamed Viktor Rudnev, William Elgharib, Lingjie Smith, Vladislav Liu, Christian Golyanik, Theobalt","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Srinivasan et al., 2021] Pratul P Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T Barron. Nerv: Neural reflectance and visibility fields for relighting and view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 7495-7504, 2021.","title":"Nerv: Neural reflectance and visibility fields for relighting and view synthesis","authors":"Boyang Pratul P Srinivasan, Xiuming Deng, Matthew Zhang, Ben Tancik, Jonathan T Mildenhall, Barron","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Sun et al., 2023] Jia-Mu Sun, Tong Wu, Yong-Liang Yang, Yu-Kun Lai, and Lin Gao. Sol-nerf: Sunlight modeling for outdoor scene decomposition and relighting. In SIG- GRAPH Asia 2023 Conference Papers, pages 1-11, 2023.","title":"Sol-nerf: Sunlight modeling for outdoor scene decomposition and relighting","authors":"Jia-Mu Sun, Tong Wu, Yong-Liang Yang, Yu-Kun Lai, Lin Gao","year":"2023","venue":"SIG-GRAPH Asia 2023 Conference Papers","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Sun et al., 2024] Cheng Sun, Jaesung Choe, Charles Loop, Wei-Chiu Ma, and Yu-Chiang Frank Wang. Sparse voxels rasterization: Real-time high-fidelity radiance field render- ing. arXiv preprint arXiv:2412.04459, 2024.","title":"Sparse voxels rasterization: Real-time high-fidelity radiance field rendering","authors":"Cheng Sun, Jaesung Choe, Charles Loop, Wei-Chiu Ma, Yu-Chiang Frank, Wang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Tian et al., 2018] Fei-Peng Tian, Wei Feng, Qian Zhang, Xiaowei Wang, Jizhou Sun, Vincenzo Loia, and Zhi-Qiang Liu. Active camera relocalization from a single reference image without hand-eye calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(12):2791- 2806, 2018.","title":"Active camera relocalization from a single reference image without hand-eye calibration","authors":"Fei-Peng Tian, Wei Feng, Qian Zhang, Xiaowei Wang, Jizhou Sun, Vincenzo Loia, Zhi-Qiang Liu","year":"2018","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2004] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transac- tions on image processing, 13(4):600-612, 2004.","title":"Image quality assessment: from error visibility to structural similarity","authors":"Zhou Wang, Alan C Bovik, Hamid R Sheikh, Eero P Simoncelli","year":"2004","venue":"IEEE transactions on image processing","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wu et al., 2023] Tong Wu, Jia-Mu Sun, Yu-Kun Lai, and Lin Gao. De-nerf: Decoupled neural radiance fields for view-consistent appearance editing and high-frequency environmental relighting. In ACM SIGGRAPH 2023 con- ference proceedings, pages 1-11, 2023.","title":"De-nerf: Decoupled neural radiance fields for view-consistent appearance editing and high-frequency environmental relighting","authors":"Tong Wu, Jia-Mu Sun, Yu-Kun Lai, Lin Gao","year":"2023","venue":"ACM SIGGRAPH 2023 conference proceedings","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Xie et al., 2024] Tianyi Xie, Zeshun Zong, Yuxing Qiu, Xuan Li, Yutao Feng, Yin Yang, and Chenfanfu Jiang. Physgaussian: Physics-integrated 3d gaussians for gener- ative dynamics. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 4389-4398, 2024.","title":"Physgaussian: Physics-integrated 3d gaussians for generative dynamics","authors":"Tianyi Xie, Zeshun Zong, Yuxing Qiu, Xuan Li, Yutao Feng, Yin Yang, Chenfanfu Jiang","year":"2024","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Xu et al., 2018] Zexiang Xu, Kalyan Sunkavalli, Sunil Hadap, and Ravi Ramamoorthi. Deep image-based re- lighting from optimal sparse samples. ACM Transactions on Graphics (ToG), 37(4):1-13, 2018.","title":"Deep image-based relighting from optimal sparse samples","authors":"Xu","year":"2018","venue":"ACM Transactions on Graphics (ToG)","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xu et al., 2023] Yingyan Xu, Gaspard Zoss, Prashanth Chandran, Markus Gross, Derek Bradley, and Paulo Go- tardo. Renerf: Relightable neural radiance fields with nearfield lighting. In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision, pages 22581- 22591, 2023.","title":"Renerf: Relightable neural radiance fields with nearfield lighting","authors":"Xu","year":"2023","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Yang et al., 2022] Wenqi Yang, Guanying Chen, Chaofeng Chen, Zhenfang Chen, and Kwan-Yee K Wong. Ps- nerf: Neural inverse rendering for multi-view photometric stereo. In European conference on computer vision, pages 266-284. Springer, 2022.","title":"Psnerf: Neural inverse rendering for multi-view photometric stereo","authors":"Wenqi Yang, Guanying Chen, Chaofeng Chen, Zhenfang Chen, Kwan-Yee K Wong","year":"2022","venue":"European conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Yu and Smith, 2019] Ye Yu and William AP Smith. In- verserendernet: Learning single image inverse rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3155-3164, 2019.","title":"Inverserendernet: Learning single image inverse rendering","authors":"Smith ; ] Ye Yu, William Ap Yu, Smith","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Yu et al., 2020] Ye Yu, Abhimitra Meka, Mohamed El- gharib, Hans-Peter Seidel, Christian Theobalt, and William AP Smith. Self-supervised outdoor scene relight- ing. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceed- ings, Part XXII 16, pages 84-101. Springer, 2020.","title":"Self-supervised outdoor scene relighting","authors":"Yu","year":"2020","venue":"Computer Vision-ECCV 2020: 16th European Conference","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yu et al., 2024] Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splatting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 19447-19456, 2024.","title":"Mip-splatting: Alias-free 3d gaussian splatting","authors":"Yu","year":"2024","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhang et al., 2018] Qian Zhang, Wei Feng, Liang Wan, Fei- Peng Tian, and Ping Tan. Active recurrence of lighting condition for fine-grained change detection. In IJCAI, pages 4972-4978, 2018.","title":"Active recurrence of lighting condition for fine-grained change detection","authors":"Qian Zhang, Wei Feng, Liang Wan, Ping Fei-Peng Tian, Tan","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/690"},{"position":40,"raw":"[Zhang et al., 2020] Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun. Nerf++: Analyzing and improving neural radiance fields. arXiv preprint arXiv:2010.07492, 2020.","title":"Nerf++: Analyzing and improving neural radiance fields","authors":"Kai Zhang, Gernot Riegler, Noah Snavely, Vladlen Koltun","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhang et al., 2021a] Kai Zhang, Fujun Luan, Qianqian Wang, Kavita Bala, and Noah Snavely. Physg: Inverse ren- dering with spherical gaussians for physics-based material editing and relighting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5453-5462, 2021.","title":"Physg: Inverse rendering with spherical gaussians for physics-based material editing and relighting","authors":"Zhang","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhang et al., 2021b] Xiuming Zhang, Pratul P Srinivasan, Boyang Deng, Paul Debevec, William T Freeman, and Jonathan T Barron. Nerfactor: Neural factorization of shape and reflectance under an unknown illumination. ACM Transactions on Graphics (ToG), 40(6):1-18, 2021.","title":"Nerfactor: Neural factorization of shape and reflectance under an unknown illumination","authors":"Zhang","year":"2021","venue":"ACM Transactions on Graphics (ToG)","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang et al., 2022] Yuanqing Zhang, Jiaming Sun, Xingyi He, Huan Fu, Rongfei Jia, and Xiaowei Zhou. Modeling indirect illumination for inverse rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 18643-18652, 2022.","title":"Modeling indirect illumination for inverse rendering","authors":"Yuanqing Zhang, Jiaming Sun, Xingyi He, Huan Fu, Rongfei Jia, Xiaowei Zhou","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2024] Qi Zhang, Chi Huang, Qian Zhang, Nan Li, and Wei Feng. Learning geometry consistent neural radiance fields from sparse and unposed views. In Pro- ceedings of the 32nd ACM International Conference on Multimedia, pages 8508-8517, 2024.","title":"Learning geometry consistent neural radiance fields from sparse and unposed views","authors":"Qi Zhang, Chi Huang, Qian Zhang, Nan Li, Wei Feng","year":"2024","venue":"Proceedings of the 32nd ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}