{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:44:52Z","timestamp":1777455892272,"version":"3.51.4"},"reference-count":186,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["Grant 62272475, Grant 62102433, and Grant 62090023"],"award-info":[{"award-number":["Grant 62272475, Grant 62102433, and Grant 62090023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Laboratory of Advanced Microprocessor Chips and Systems"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>Recent strides in hardware-accelerated ray tracing have propelled algorithms once deemed suitable only for offline rendering, like Monte Carlo path tracing, into interactive frame rates. While path tracing has been regarded as a practical utility in animating scenes for the film industry, achieving visually noise-free imagery often mandates thousands of samples per pixel and considerable computation time. Regrettably, this poses a difficulty for video games and virtual reality applications, which demand high frame rates and resolutions, thereby constraining the computational overhead of path tracing. Two extant approaches, in-process sampling, and post-processing reconstruction methods, i.e., denoising and upsampling, address this challenge. The giant evolution of deep learning technology has emerged as pivotal in path tracing processing. We explore and advance Monte Carlo path tracing technology based on deep learning. Moreover, we illustrate the merits and demerits of diverse designs and technologies, propose potential future development trends, and aim at providing researchers with a comprehensive understanding of the cutting-edge in deep learning-driven Monte Carlo path tracing.<\/jats:p>","DOI":"10.1145\/3768618","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T11:35:07Z","timestamp":1758281707000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A Survey on Deep Learning for Monte Carlo Path Tracing"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7064-4838","authenticated-orcid":false,"given":"Run","family":"Yan","sequence":"first","affiliation":[{"name":"National University of Defense Technology","place":["Changsha, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5131-0437","authenticated-orcid":false,"given":"Hui","family":"Guo","sequence":"additional","affiliation":[{"name":"National University of Defense Technology","place":["Changsha, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7878-3998","authenticated-orcid":false,"given":"Libo","family":"Huang","sequence":"additional","affiliation":[{"name":"National University of Defense Technology","place":["Changsha, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2166-977X","authenticated-orcid":false,"given":"Nong","family":"Xiao","sequence":"additional","affiliation":[{"name":"National University of Defense Technology","place":["Changsha, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9043-2998","authenticated-orcid":false,"given":"Shen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computing Science, National University of Defense Technology","place":["Changsha, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2514-2052","authenticated-orcid":false,"given":"Yongwen","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Defense Technology","place":["Changsha, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2498-7414","authenticated-orcid":false,"given":"Yashuai","family":"Lv","sequence":"additional","affiliation":[{"name":"QiYuan Lab","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4234-1359","authenticated-orcid":false,"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"AMD 2023. AMD FidelityFX\u2122 Super Resolution. Retrieved from https:\/\/www.amd.com\/en\/products\/graphics\/technologies\/fidelityfx\/super-resolution.html"},{"key":"e_1_3_2_3_2","unstructured":"AMD 2023. AMD Radeon\u2122 RX 7600 XT. Retrieved from https:\/\/www.amd.com\/en\/products\/graphics\/amd-radeon-rx-7600-xt"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","unstructured":"Saeed Anwar Salman Khan and Nick Barnes. 2020. A deep journey into super-resolution: A survey. ACM Computing Surveys 53 3(2020) 34 pages. DOI:10.1145\/3390462","DOI":"10.1145\/3390462"},{"key":"e_1_3_2_5_2","article-title":"Wasserstein GAN","volume":"1701","author":"Arjovsky Mart\u00edn","year":"2017","unstructured":"Mart\u00edn Arjovsky, Soumith Chintala, and L\u00e9on Bottou. 2017. Wasserstein GAN. ArXiv abs\/1701.07875 (2017). Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:13943041","journal-title":"ArXiv"},{"key":"e_1_3_2_6_2","unstructured":"Arm 2023. Immortalis-G715. Retrieved from https:\/\/www.arm.com\/products\/silicon-ip-multimedia\/immortalis-gpu\/immortalis-g715"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417847"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","DOI":"10.1145\/3528233.3530730","article-title":"Self-supervised post-correction for Monte Carlo denoising","author":"Back Jonghee","year":"2022","unstructured":"Jonghee Back, Binh-Son Hua, Toshiya Hachisuka, and Bochang Moon. 2022. Self-supervised post-correction for Monte Carlo denoising. ACM SIGGRAPH 2022 Conference Proceedings (2022), 1\u20138. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:250702225","journal-title":"ACM SIGGRAPH 2022 Conference Proceedings"},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","DOI":"10.1145\/3610548.3618177","article-title":"Input-Dependent Uncorrelated Weighting for Monte Carlo Denoising","author":"Back Jonghee","year":"2023","unstructured":"Jonghee Back, Binh-Son Hua, Toshiya Hachisuka, and Bochang Moon. 2023. Input-Dependent Uncorrelated Weighting for Monte Carlo Denoising. SIGGRAPH Asia 2023 Conference Papers (2023), 1\u201310. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:266177117","journal-title":"SIGGRAPH Asia 2023 Conference Papers"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2007.4408903"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13858"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073708"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/882262.882318"},{"key":"e_1_3_2_14_2","article-title":"Neural partitioning pyramids for denoising Monte Carlo renderings","author":"Balint Martin","year":"2023","unstructured":"Martin Balint, Krzysztof Wolski, Karol Myszkowski, Hans-Peter Seidel, and Rafa\u0142 K. Mantiuk. 2023. Neural partitioning pyramids for denoising Monte Carlo renderings. ACM SIGGRAPH 2023 Conference Proceedings (2023), 1\u201311. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:259644461","journal-title":"ACM SIGGRAPH 2023 Conference Proceedings"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00382"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759561"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2011.01996.x"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073698"},{"key":"e_1_3_2_19_2","article-title":"Nonlinearly weighted first\u2010order regression for denoising Monte Carlo renderings","volume":"35","author":"Bitterli Benedikt","year":"2016","unstructured":"Benedikt Bitterli, Fabrice Rousselle, Bochang Moon, Jose A. Iglesias-Guitian, David Adler, Kenny Mitchell, Wojciech Jarosz, and Jan Nov\u00e1k. 2016. Nonlinearly weighted first\u2010order regression for denoising Monte Carlo renderings. Computer Graphics Forum 35, 4 (2016), 107\u2013117. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:1057460","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_20_2","first-page":"148:1\u2013148:17","article-title":"Spatiotemporal reservoir resampling for real-time ray tracing with dynamic direct lighting","volume":"39","author":"Bitterli Benedikt","year":"2020","unstructured":"Benedikt Bitterli, Chris Wyman, Matt Pharr, Peter Shirley, Aaron E. Lefohn, and Wojciech Jarosz. 2020. Spatiotemporal reservoir resampling for real-time ray tracing with dynamic direct lighting. ACM Transactions on Graphics 39, 4 (2020), 148:1\u2013148:17. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:218672542","journal-title":"ACM Transactions on Graphics"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3478513.3480553"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3588432.3591497"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-005-0287-1"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073601"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2004.1315043"},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","DOI":"10.1145\/3610543.3626179","article-title":"Monte Carlo denoising via multi-scale auxiliary feature fusion guided transformer","author":"Chen Bingyi","year":"2023","unstructured":"Bingyi Chen, Zengyu Liu, Li Yuan, Zhitao Liu, Yi Li, Guan Wang, and Ning Xie. 2023. Monte Carlo denoising via multi-scale auxiliary feature fusion guided transformer. SIGGRAPH Asia 2023 Technical Communications (2023), 1\u20134. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:265450029","journal-title":"SIGGRAPH Asia 2023 Technical Communications"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3450626.3459876","article-title":"Weakly-supervised contrastive learning in path manifold for Monte Carlo image reconstruction","volume":"40","author":"Cho I","year":"2021","unstructured":"I Cho, Yuchi Huo, and Sung eui Yoon. 2021. Weakly-supervised contrastive learning in path manifold for Monte Carlo image reconstruction. ACM Transactions on Graphics 40, 4 (2021), 1\u201314. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:236004240","journal-title":"ACM Transactions on Graphics"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14406"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/1073204.1073328"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2010.77"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3084363.3085032"},{"issue":"4","key":"e_1_3_2_32_2","first-page":"58.1\u201358.41","article-title":"Toward real-time ray tracing: A survey on hardware acceleration and microarchitecture techniques","volume":"50","author":"Deng Yangdong","year":"2017","unstructured":"Yangdong Deng, Yufei Ni, Zonghui Li, Shuai Mu, and Wenjun Zhang. 2017. Toward real-time ray tracing: A survey on hardware acceleration and microarchitecture techniques. Computing Surveys 50, 4 (2017), 58.1\u201358.41.","journal-title":"Computing Surveys"},{"key":"e_1_3_2_33_2","volume-title":"Proceedings of the Eurographics Symposium on Rendering","author":"d\u2019Eon Eugene","year":"2021","unstructured":"Eugene d\u2019Eon and Jan Nov\u00e1k. 2021. Zero-variance transmittance estimation. In Proceedings of the Eurographics Symposium on Rendering. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:235615645"},{"key":"e_1_3_2_34_2","volume-title":"Proceedings of the North American Chapter of the Association for Computational Linguistics","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the North American Chapter of the Association for Computational Linguistics. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:52967399"},{"key":"e_1_3_2_35_2","first-page":"13728","article-title":"RepVGG: Making VGG-style convNets great again","author":"Ding Xiaohan","year":"2021","unstructured":"Xiaohan Ding, X. Zhang, Ningning Ma, Jungong Han, Guiguang Ding, and Jian Sun. 2021. RepVGG: Making VGG-style convNets great again. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021), 13728\u201313737. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:231572790","journal-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_2_36_2","article-title":"NICE: Non-linear independent components estimation","volume":"1410","author":"Dinh Laurent","year":"2014","unstructured":"Laurent Dinh, David Krueger, and Yoshua Bengio. 2014. NICE: Non-linear independent components estimation. CoRR abs\/1410.8516 (2014). Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:13995862","journal-title":"CoRR"},{"key":"e_1_3_2_37_2","volume-title":"5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings","author":"Dinh Laurent","year":"2017","unstructured":"Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. 2017. Density estimation using Real NVP. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=HkpbnH9lx"},{"key":"e_1_3_2_38_2","article-title":"Neural parametric mixtures for path guiding","author":"Dong Honghao","year":"2023","unstructured":"Honghao Dong, Guoping Wang, and Sheng Li. 2023. Neural parametric mixtures for path guiding. ACM SIGGRAPH 2023 Conference Proceedings (2023), 1\u201310. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:259975871","journal-title":"ACM SIGGRAPH 2023 Conference Proceedings"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3556544"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","unstructured":"Fr\u00e9do Durand Nicolas Holzschuch Cyril Soler Eric Chan and Fran\u00e7ois X. Sillion. 2005. A frequency analysis of light transport. ACM Transactions on Graphics (TOG\u201905) 24 3 (2005) 1115\u20131126. 10.1145\/1073204.1073320. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:1800833","DOI":"10.1145\/1073204.1073320"},{"key":"e_1_3_2_41_2","article-title":"Real\u2010time Monte Carlo denoising with weight sharing kernel prediction network","volume":"40","author":"Fan Hangming","year":"2021","unstructured":"Hangming Fan, Rui Wang, Yuchi Huo, and Hujun Bao. 2021. Real\u2010time Monte Carlo denoising with weight sharing kernel prediction network. Computer Graphics Forum 40, 4 (2021), 15\u201327. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:235825101","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2024.3397828"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14454"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3588432.3591537"},{"key":"e_1_3_2_45_2","article-title":"Monte Carlo denoising with a sparse auxiliary feature encoder","author":"Fu Siyuan","year":"2021","unstructured":"Siyuan Fu, Yifan Lu, Xiao Hua Zhang, and Ning Xie. 2021. Monte Carlo denoising with a sparse auxiliary feature encoder. SIGGRAPH Asia 2021 Posters (2021), 1\u20132. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:245021559","journal-title":"SIGGRAPH Asia 2021 Posters"},{"key":"e_1_3_2_46_2","volume-title":"Unreal Engine 4","author":"Games Epic","year":"2014","unstructured":"Epic Games. 2014. Unreal Engine 4. Retrieved from https:\/\/www.unrealengine.com"},{"key":"e_1_3_2_47_2","unstructured":"Kyle Gao Yin Gao Hongjie He Denning Lu Linlin Xu and Jonathan Li. 2022. NeRF: Neural Radiance Field in 3D Vision A Comprehensive Review. arXiv:2210.00379. Retrieved from https:\/\/arxiv.org\/abs\/2210.00379. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:252683586"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2938758"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3322954"},{"key":"e_1_3_2_50_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Goodfellow Ian J.","year":"2014","unstructured":"Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the Neural Information Processing Systems. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:261560300"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392475"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459848"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201363"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3550454.3555496"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366183"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3478513.3480531"},{"key":"e_1_3_2_57_2","first-page":"1","article-title":"GradNet: Unsupervised deep screened poisson reconstruction for gradient-domain rendering","volume":"38","author":"Guo Jie","year":"2019","unstructured":"Jie Guo, Mengtian Li, Quewei Li, Yuting Qiang, Bingyang Hu, Yanwen Guo, and Ling-Qi Yan. 2019. GradNet: Unsupervised deep screened poisson reconstruction for gradient-domain rendering. ACM Transactions on Graphics 38, 6 (2019), 1\u201313. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:202754826","journal-title":"ACM Transactions on Graphics"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3610548.3618221"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2004.1309458"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/1360612.1360632"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/1661412.1618487"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3585505"},{"key":"e_1_3_2_63_2","article-title":"Neural temporal adaptive sampling and denoising","volume":"39","author":"Hasselgren Jon","year":"2020","unstructured":"Jon Hasselgren, Jacob Munkberg, Marco Salvi, Anjul Patney, and Aaron E. Lefohn. 2020. Neural temporal adaptive sampling and denoising. Computer Graphics Forum 39, 2 (2020), 147\u2013155. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:221583018","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.213"},{"key":"e_1_3_2_65_2","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He Kaiming","year":"2015","unstructured":"Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (2015), 770\u2013778. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:206594692","journal-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_2_66_2","unstructured":"Ruian He Shili Zhou Yuqi Sun Ri Cheng Weimin Tan and Bo Yan. 2023. Low-latency space-time supersampling for real-time rendering. arXiv:2312.10890. Retrieved from https:\/\/arxiv.org\/abs\/2312.10890. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:266359220"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/3744898"},{"key":"e_1_3_2_68_2","article-title":"Intel XeSS \u2013 an AI based super sampling solution for real-time rendering","author":"Kawiak Rense Robert de Boer Gabriel Ferreira Hisham Chowdhury,","year":"2022","unstructured":"Rense Robert de Boer Gabriel Ferreira Hisham Chowdhury, Kawiak and Lucas Xavier. 2022. Intel XeSS \u2013 an AI based super sampling solution for real-time rendering. In Game Developers Conference (2022).","journal-title":"In Game Developers Conference"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1145\/3585497"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1145\/3406181"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3588432.3591493"},{"key":"e_1_3_2_72_2","article-title":"A survey on gradient\u2010domain rendering","volume":"38","author":"Hua Binh-Son","year":"2019","unstructured":"Binh-Son Hua, Adrien Gruson, Victor Petitjean, Matthias Zwicker, Derek Nowrouzezahrai, Elmar Eisemann, and Toshiya Hachisuka. 2019. A survey on gradient\u2010domain rendering. Computer Graphics Forum 38, 2 (2019), 455\u2013472. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:142458909","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2008.919360"},{"key":"e_1_3_2_74_2","first-page":"2261","article-title":"Densely connected convolutional networks","author":"Huang Gao","year":"2016","unstructured":"Gao Huang, Zhuang Liu, and Kilian Q. Weinberger. 2016. Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (2016), 2261\u20132269. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:9433631","journal-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/3649310"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.1999.786990"},{"key":"e_1_3_2_77_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Huang Yan","year":"2015","unstructured":"Yan Huang, Wei Wang, and Liang Wang. 2015. Bidirectional recurrent convolutional networks for multi-frame super-resolution. In Proceedings of the Neural Information Processing Systems. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:56486313"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41095-021-0209-9"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1145\/3368313"},{"key":"e_1_3_2_80_2","unstructured":"Intel 2023. Intel\u00ae Open Image Denoise. Retrieved from https:\/\/www.openimagedenoise.org\/."},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1016\/1049-9652(91)90045-L"},{"key":"e_1_3_2_82_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3450626.3459793","article-title":"Interactive Monte Carlo denoising using affinity of neural features","volume":"40","author":"I\u015f\u0131k Mustafa Serkan","year":"2021","unstructured":"Mustafa Serkan I\u015f\u0131k, Matthew Fisher, Jonathan Eisenmann, and Micha\u00ebl Gharbi. 2021. Interactive Monte Carlo denoising using affinity of neural features. ACM Transactions on Graphics 40, 4 (2021), 1\u201313. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:235128595","journal-title":"ACM Transactions on Graphics"},{"key":"e_1_3_2_83_2","unstructured":"Susmija Jabbireddy Shuo Li Xiaoxu Meng Judith E. Terrill and Amitabh Varshney. 2023. Accelerated volume rendering with volume guided neural denoising. Retrieved June 2024 from https:\/\/api.semanticscholar.org\/CorpusID:259301019"},{"key":"e_1_3_2_84_2","unstructured":"William James and Charles M. Stein. 1992. Estimation with quadratic Loss. Retrieved June 2024 from https:\/\/api.semanticscholar.org\/CorpusID:17984683"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cag.2020.09.007"},{"key":"e_1_3_2_86_2","doi-asserted-by":"crossref","unstructured":"Justin Johnson Alexandre Alahi and Li Fei-Fei. 2016. Perceptual losses for real-time style transfer and super-resolution. arXiv:1603.08155. Retrieved from https:\/\/arxiv.org\/abs\/1603.08155. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:980236","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1145\/15886.15902"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1145\/2766977"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1145\/3130800.3130880"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1631\/FITEE.1500251"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2007.4429281"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.3390\/sym11091066"},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1145\/2776880.2792699"},{"key":"e_1_3_2_94_2","doi-asserted-by":"publisher","DOI":"10.1145\/3592433"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3323038"},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","unstructured":"Alex Krizhevsky I. Sutskever and G. Hinton. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM 60 6 (2017) 84\u201390. 10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073665"},{"key":"e_1_3_2_98_2","article-title":"Deep adaptive sampling for low sample count rendering","volume":"37","author":"Kuznetsov Alexandr","year":"2018","unstructured":"Alexandr Kuznetsov, Nima Khademi Kalantari, and Ravi Ramamoorthi. 2018. Deep adaptive sampling for low sample count rendering. Computer Graphics Forum 37, 4 (2018), 35\u201344. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:49341310","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_99_2","first-page":"20701","article-title":"SteerNeRF: Accelerating NeRF rendering via smooth viewpoint trajectory","author":"Li Sicheng","year":"2022","unstructured":"Sicheng Li, Hao Li, Yue Wang, Yiyi Liao, and Lu Yu. 2022. SteerNeRF: Accelerating NeRF rendering via smooth viewpoint trajectory. 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022), 20701\u201320711. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:254823576","journal-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818084"},{"key":"e_1_3_2_101_2","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366213"},{"key":"e_1_3_2_102_2","first-page":"1132","article-title":"Enhanced deep residual networks for single image super-resolution","author":"Lim Bee","year":"2017","unstructured":"Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. 2017. Enhanced deep residual networks for single image super-resolution. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (2017), 1132\u20131140. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:6540453","journal-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops"},{"key":"e_1_3_2_103_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41095-020-0167-7"},{"key":"e_1_3_2_104_2","article-title":"Path\u2010based Monte Carlo denoising using a three\u2010scale neural network","volume":"40","author":"Lin Weiheng","year":"2020","unstructured":"Weiheng Lin, Beibei Wang, Jian Yang, Lu Wang, and Ling-Qi Yan. 2020. Path\u2010based Monte Carlo denoising using a three\u2010scale neural network. Computer Graphics Forum 40, 1 (2020). Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:221764375","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1145\/3658203"},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-021-02204-4"},{"key":"e_1_3_2_108_2","article-title":"DMCR-GAN: Adversarial denoising for Monte Carlo renderings with residual attention networks and hierarchical features modulation of auxiliary buffers","author":"Lu Yifan","year":"2020","unstructured":"Yifan Lu, Ning Xie, and Heng Tao Shen. 2020. DMCR-GAN: Adversarial denoising for Monte Carlo renderings with residual attention networks and hierarchical features modulation of auxiliary buffers. SIGGRAPH Asia 2020 Technical Communications (2020), 1\u20134. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:227129200","journal-title":"SIGGRAPH Asia 2020 Technical Communications"},{"key":"e_1_3_2_109_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2024.105304"},{"key":"e_1_3_2_110_2","unstructured":"Dawid Malarz Weronika Smolak Jacek Tabor S\u0142awomir Konrad Tadeja and Przemys\u0142aw Spurek. 2023. Gaussian Splatting with NeRF-based Color and Opacity. arXiv:2312.13729. Retrieved from https:\/\/arxiv.org\/abs\/2312.13729. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:266435457"},{"key":"e_1_3_2_111_2","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2980256"},{"key":"e_1_3_2_112_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Mao Xiao-Jiao","year":"2016","unstructured":"Xiao-Jiao Mao, Chunhua Shen, and Yubin Yang. 2016. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In Proceedings of the Neural Information Processing Systems. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:10987457"},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1145\/318009.318015"},{"key":"e_1_3_2_114_2","article-title":"A survey on Bounding Volume Hierarchies for ray tracing","volume":"40","author":"Meister Daniel","year":"2021","unstructured":"Daniel Meister, Shinji Ogaki, Carsten Benthin, Michael J. Doyle, Michael Guthe, and Ji\u0159\u00ed Bittner. 2021. A survey on Bounding Volume Hierarchies for ray tracing. Computer Graphics Forum 40, 2 (2021), 683\u2013712. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:235337851","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_115_2","volume-title":"Proceedings of the Eurographics Symposium on Rendering","author":"Meng Xiaoxu","year":"2020","unstructured":"Xiaoxu Meng, Quan Zheng, Amitabh Varshney, Gurprit Singh, and Matthias Zwicker. 2020. Real-time Monte Carlo denoising with the neural bilateral grid. In Proceedings of the Eurographics Symposium on Rendering. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:220284605"},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503250"},{"key":"e_1_3_2_117_2","doi-asserted-by":"crossref","DOI":"10.1145\/3450618.3469140","article-title":"Foveated Monte-Carlo denoising","author":"Milef Nicholas","year":"2021","unstructured":"Nicholas Milef and Nima Khademi Kalantari. 2021. Foveated Monte-Carlo denoising. ACM SIGGRAPH 2021 Posters (2021), 1\u20132. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:236936988","journal-title":"ACM SIGGRAPH 2021 Posters"},{"key":"e_1_3_2_118_2","doi-asserted-by":"publisher","DOI":"10.1145\/37401.37410"},{"key":"e_1_3_2_119_2","unstructured":"Ansh Mittal. 2023. Neural Radiance Fields: Past Present and Future. arXiv:2304.10050. Retrieved from https:\/\/arxiv.org\/abs\/2304.10050. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:258236582"},{"key":"e_1_3_2_120_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"e_1_3_2_121_2","doi-asserted-by":"publisher","DOI":"10.1145\/2641762"},{"key":"e_1_3_2_122_2","doi-asserted-by":"publisher","DOI":"10.1145\/3341156"},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417804"},{"key":"e_1_3_2_124_2","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459812"},{"key":"e_1_3_2_125_2","doi-asserted-by":"crossref","DOI":"10.1111\/cgf.14049","article-title":"Neural denoising with layer embeddings","author":"Munkberg Jacob","year":"2020","unstructured":"Jacob Munkberg and Jon Hasselgren. 2020. Neural denoising with layer embeddings. Computer Graphics Forum 39, 4 (2020), 1\u201312. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:221581386","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_126_2","first-page":"5436","article-title":"Softmax splatting for video frame interpolation","author":"Niklaus Simon","year":"2020","unstructured":"Simon Niklaus and Feng Liu. 2020. Softmax splatting for video frame interpolation. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020), 5436\u20135445. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:212675709","journal-title":"2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_2_127_2","article-title":"Monte Carlo methods for volumetric light transport simulation","volume":"37","author":"Nov\u00e1k Jan","year":"2018","unstructured":"Jan Nov\u00e1k, Iliyan Georgiev, Johannes Hanika, and Wojciech Jarosz. 2018. Monte Carlo methods for volumetric light transport simulation. Computer Graphics Forum 37, 2 (2018), 551\u2013576. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:3914404","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.1145\/2661229.2661292"},{"key":"e_1_3_2_129_2","unstructured":"NVIDIA 2019. NVIDIA OptiX\u2122 Ray Tracing Engine. Retrieved May 2024 from https:\/\/developer.nvidia.com\/rtx\/ray-tracing\/optix"},{"key":"e_1_3_2_130_2","unstructured":"NVIDIA 2023. NVIDIA ADA GPU architecture. Retrieved October 2024 from https:\/\/images.nvidia.cn\/aem-dam\/Solutions\/geforce\/ada\/nvidia-ada-gpu-architecture.pdf"},{"key":"e_1_3_2_131_2","unstructured":"NVIDIA 2023. NVIDIA DLSS research. Retrieved May 2024 from https:\/\/developer.nvidia.com\/dlss\/research"},{"key":"e_1_3_2_132_2","unstructured":"NVIDIA 2023. NVIDIA OptiX\u2122 AI-Accelerated Denoiser. Retrieved October 2024 from https:\/\/developer.nvidia.com\/optix-denoiser"},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-024-03446-8"},{"key":"e_1_3_2_134_2","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073691"},{"key":"e_1_3_2_135_2","doi-asserted-by":"crossref","DOI":"10.1145\/3355088.3365150","article-title":"Faster RPNN: Rendering clouds with latent space light probes","author":"Panin Mikhail","year":"2019","unstructured":"Mikhail Panin and Sergey I. Nikolenko. 2019. Faster RPNN: Rendering clouds with latent space light probes. SIGGRAPH Asia 2019 Technical Briefs (2019), 21\u201324. Retrieved October 2024 from https:\/\/api.semanticscholar.org\/CorpusID:208140749","journal-title":"SIGGRAPH Asia 2019 Technical Briefs"},{"key":"e_1_3_2_136_2","doi-asserted-by":"publisher","DOI":"10.1145\/2619195.2656287"},{"key":"e_1_3_2_137_2","unstructured":"Olaf Ronneberger Philipp Fischer and Thomas Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. arXiv:1505.04597. Retrieved from https:\/\/arxiv.org\/abs\/1505.04597. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:3719281"},{"key":"e_1_3_2_138_2","first-page":"12","volume-title":"Proceedings of the 2011 SIGGRAPH Asia Conference","author":"Rousselle Fabrice","year":"2011","unstructured":"Fabrice Rousselle, Claude Knaus, and Matthias Zwicker. 2011. Adaptive sampling and reconstruction using greedy error minimization. In Proceedings of the 2011 SIGGRAPH Asia Conference. 12 pages."},{"key":"e_1_3_2_139_2","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366214"},{"key":"e_1_3_2_140_2","article-title":"Robust denoising using feature and color information","volume":"32","author":"Rousselle Fabrice","year":"2013","unstructured":"Fabrice Rousselle, Marco Manzi, and Matthias Zwicker. 2013. Robust denoising using feature and color information. Computer Graphics Forum 32, 7 (2013), 121\u2013130. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:10020147","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_141_2","doi-asserted-by":"publisher","DOI":"10.1145\/3550454.3555515"},{"key":"e_1_3_2_142_2","article-title":"Implementation of Random Parameter Filtering","author":"Sen Pradeep","year":"2011","unstructured":"Pradeep Sen and Soheil Darabi. 2011. Implementation of Random Parameter Filtering. University of New Mexico Technical Report. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:4685535","journal-title":"University of New Mexico Technical Report"},{"key":"e_1_3_2_143_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature16961"},{"key":"e_1_3_2_144_2","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. Retrieved from https:\/\/arxiv.org\/abs\/1409.1556. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:14124313"},{"key":"e_1_3_2_145_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176345632"},{"key":"e_1_3_2_146_2","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530183"},{"key":"e_1_3_2_147_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587659"},{"key":"e_1_3_2_148_2","article-title":"Free Path Sampling in High Resolution Inhomogeneous Participating Media","volume":"30","author":"Szirmay-Kalos L\u00e1szl\u00f3","year":"2011","unstructured":"L\u00e1szl\u00f3 Szirmay-Kalos, Bal\u00e1zs T\u00f3th, and Mil\u00e1n Magdics. 2011. Free Path Sampling in High Resolution Inhomogeneous Participating Media. Computer Graphics Forum 30, 1 (2011), 85\u201397. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:17144778","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_149_2","first-page":"285-\u2013296","volume-title":"Proceedings of the Eurographics Workshop on Rendering Techniques","author":"Tamstorf Rasmus","year":"1997","unstructured":"Rasmus Tamstorf and Henrik Wann Jensen. 1997. Adaptive smpling and bias estimation in path tracing. In Proceedings of the Eurographics Workshop on Rendering Techniques. Springer-Verlag, Berlin, 285-\u2013296."},{"key":"e_1_3_2_150_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-60801-3_27"},{"key":"e_1_3_2_151_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14022"},{"key":"e_1_3_2_152_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543870"},{"key":"e_1_3_2_153_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Neural Information Processing Systems. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:13756489"},{"key":"e_1_3_2_154_2","doi-asserted-by":"publisher","DOI":"10.1145\/218380.218498"},{"key":"e_1_3_2_155_2","doi-asserted-by":"publisher","DOI":"10.1145\/258734.258775"},{"key":"e_1_3_2_156_2","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201388"},{"key":"e_1_3_2_157_2","article-title":"Classifier guided temporal supersampling for real\u2010time rendering","volume":"41","author":"Vouga Etienne","year":"2022","unstructured":"Etienne Vouga, Christopher Wojtan, Yu-Xiao Guo, Guojun Chen, Yue Dong, and Xin Tong. 2022. Classifier guided temporal supersampling for real\u2010time rendering. Computer Graphics Forum 41, 7 (2022), 237\u2013246. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:254249752","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_158_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11633-022-1400-x"},{"key":"e_1_3_2_159_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2982166"},{"key":"e_1_3_2_160_2","doi-asserted-by":"publisher","DOI":"10.1145\/54852.378490"},{"key":"e_1_3_2_161_2","unstructured":"Xinyue Wei Haozhi Huang Yujin Shi Hongliang Yuan Li Shen and Jue Wang. 2021. End-to-end adaptive Monte Carlo denoising and super-resolution. arXiv:2108.06915. Retrieved from https:\/\/arxiv.org\/abs\/2108.06915. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:237091339"},{"key":"e_1_3_2_162_2","doi-asserted-by":"publisher","DOI":"10.5555\/2032617.2032641"},{"key":"e_1_3_2_163_2","doi-asserted-by":"crossref","DOI":"10.1145\/3283254.3283261","article-title":"Robust deep residual denoising for Monte Carlo rendering","author":"Wong Kin-Ming","year":"2018","unstructured":"Kin-Ming Wong and T. Wong. 2018. Robust deep residual denoising for Monte Carlo rendering. SIGGRAPH Asia 2018 Technical Briefs (2018), 1\u20134. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:54434252","journal-title":"SIGGRAPH Asia 2018 Technical Briefs"},{"key":"e_1_3_2_164_2","first-page":"142:1\u2013142:12","article-title":"Neural supersampling for real-time rendering","volume":"39","author":"Xiao Lei","year":"2020","unstructured":"Lei Xiao, Salah Nouri, Matthew Chapman, Alexander Fix, Douglas Lanman, and Anton Kaplanyan. 2020. Neural supersampling for real-time rendering. ACM Transactions on Graphics 39, 4 (2020), 142:1\u2013142:12. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:221105079","journal-title":"ACM Transactions on Graphics"},{"key":"e_1_3_2_165_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2999891"},{"key":"e_1_3_2_166_2","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356547"},{"key":"e_1_3_2_167_2","article-title":"Unsupervised image reconstruction for gradient\u2010domain volumetric rendering","volume":"39","author":"Xu Zilin","year":"2020","unstructured":"Zilin Xu, Qiang Sun, Lu Wang, Yanning Xu, and Beibei Wang. 2020. Unsupervised image reconstruction for gradient\u2010domain volumetric rendering. Computer Graphics Forum 39, 7 (2020), 193\u2013203. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:227128739","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_168_2","first-page":"372","volume-title":"Proceedings of the Computer Vision.","author":"Yang Chih-Yuan","year":"2014","unstructured":"Chih-Yuan Yang, Chao Ma, and Ming-Hsuan Yang. 2014. Single-image super-resolution: A benchmark. In Proceedings of the Computer Vision.David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars (Eds.), Springer International Publishing, Cham, 372\u2013386."},{"key":"e_1_3_2_169_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2023.3259141"},{"key":"e_1_3_2_170_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-019-1964-2"},{"key":"e_1_3_2_171_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2886005"},{"key":"e_1_3_2_172_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_2_173_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00457"},{"key":"e_1_3_2_174_2","doi-asserted-by":"publisher","DOI":"10.1145\/3478513.3480565"},{"key":"e_1_3_2_175_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-020-0264-1"},{"key":"e_1_3_2_176_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14337"},{"key":"e_1_3_2_177_2","article-title":"Automatic feature selection for denoising volumetric renderings","volume":"41","author":"Zhang Xianyao","year":"2022","unstructured":"Xianyao Zhang, Melvin Ott, Marco Manzi, Markus H. Gross, and Marios Papas. 2022. Automatic feature selection for denoising volumetric renderings. Computer Graphics Forum 41, 4 (2022), 63\u201377. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:251161500","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_178_2","unstructured":"Yulun Zhang Kunpeng Li Kai Li Lichen Wang Bineng Zhong and Yun Raymond Fu. 2018. Image super-resolution using very deep residual channel attention networks. arXiv:1807.02758. Retrieved from https:\/\/arxiv.org\/abs\/1807.02758. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:49657846"},{"key":"e_1_3_2_179_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-32003-8_56-1"},{"key":"e_1_3_2_180_2","article-title":"Learning to importance sample in primary sample space","volume":"38","author":"Zheng Quan","year":"2018","unstructured":"Quan Zheng and Matthias Zwicker. 2018. Learning to importance sample in primary sample space. Computer Graphics Forum 38, 2 (2018), 169\u2013179. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:52076013","journal-title":"Computer Graphics Forum"},{"key":"e_1_3_2_181_2","doi-asserted-by":"publisher","DOI":"10.1145\/3610548.3618209"},{"key":"e_1_3_2_182_2","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459798"},{"key":"e_1_3_2_183_2","unstructured":"Shilin Zhu. 2020. Survey: Machine learning in production rendering. arXiv:2005.12518. Retrieved from https:\/\/arxiv.org\/abs\/2005.12518. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:218889618"},{"key":"e_1_3_2_184_2","first-page":"1","article-title":"Hierarchical neural reconstruction for path guiding using hybrid path and photon samples","volume":"40","author":"Zhu Shilin","year":"2021","unstructured":"Shilin Zhu, Zexiang Xu, Tiancheng Sun, Alexandr Kuznetsov, Mark Meyer, Henrik Wann Jensen, Hao Su, and Ravi Ramamoorthi. 2021. Hierarchical neural reconstruction for path guiding using hybrid path and photon samples. ACM Transactions on Graphics 40, 4 (2021), 1\u201316. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:235079316","journal-title":"ACM Transactions on Graphics"},{"key":"e_1_3_2_185_2","first-page":"1","article-title":"Photon-driven neural reconstruction for path guiding","volume":"41","author":"Zhu Shilin","year":"2021","unstructured":"Shilin Zhu, Zexiang Xu, Tiancheng Sun, Alexandr Kuznetsov, Mark Meyer, Henrik Wann Jensen, Hao Su, and Ravi Ramamoorthi. 2021. Photon-driven neural reconstruction for path guiding. ACM Transactions on Graphics 41, 1 (2021), 1\u201315. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:244035804","journal-title":"ACM Transactions on Graphics"},{"key":"e_1_3_2_186_2","article-title":"Denoising production volumetric rendering","author":"Zhu Shilin","year":"2023","unstructured":"Shilin Zhu, Xianyao Zhang, Gerhard R\u00f6thlin, Marios Papas, and Mark Meyer. 2023. Denoising production volumetric rendering. ACM SIGGRAPH 2023 Talks (2023), 1\u20132. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:260683391","journal-title":"ACM SIGGRAPH 2023 Talks"},{"key":"e_1_3_2_187_2","article-title":"Recent advances in adaptive sampling and reconstruction for Monte Carlo rendering","volume":"34","author":"Zwicker Matthias","year":"2015","unstructured":"Matthias Zwicker, Wojciech Jarosz, Jaakko Lehtinen, Bochang Moon, Ravi Ramamoorthi, Fabrice Rousselle, Pradeep Sen, Cyril Soler, and Sung eui Yoon. 2015. Recent advances in adaptive sampling and reconstruction for Monte Carlo rendering. Computer Graphics Forum 34, 2 (2015), 667\u2013681. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:5215817","journal-title":"Computer Graphics Forum"}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3768618","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T11:47:58Z","timestamp":1761565678000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3768618"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":186,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,3,31]]}},"alternative-id":["10.1145\/3768618"],"URL":"https:\/\/doi.org\/10.1145\/3768618","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,27]]},"assertion":[{"value":"2024-08-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-31","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}