{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:43:42Z","timestamp":1779295422425,"version":"3.51.4"},"reference-count":70,"publisher":"Tsinghua University Press","issue":"2","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T00:00:00Z","timestamp":1616976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Comp. Visual. Med."],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s41095-021-0209-9","type":"journal-article","created":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T08:02:31Z","timestamp":1617004951000},"page":"169-185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["A survey on deep learning-based Monte Carlo denoising"],"prefix":"10.26599","volume":"7","author":[{"given":"Yuchi","family":"Huo","sequence":"first","affiliation":[{"name":"KAIST, Daejeon, 31414, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung-eui","family":"Yoon","sequence":"additional","affiliation":[{"name":"KAIST, Daejeon, 31414, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"11138","reference":[{"key":"209_CR1","doi-asserted-by":"crossref","unstructured":"Kajiya, J. T. The rendering equation. In: Proceedings of the 13th Annual Conference on Computer Graphics and Interactive Techniques, 143\u2013150, 1986.","DOI":"10.1145\/15886.15902"},{"key":"209_CR2","unstructured":"Pharr, M.; Jakob, W.; Humphreys, G. Physically based Rendering: From Theory to Implementation. Morgan Kaufmann, 2016."},{"key":"209_CR3","doi-asserted-by":"publisher","DOI":"10.1002\/9781118631980","volume-title":"Simulation and the Monte Carlo Method","author":"R Y Rubinstein","year":"2016","unstructured":"Rubinstein, R. Y.; Kroese, D. P. Simulation and the Monte Carlo Method. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016."},{"issue":"7553","key":"209_CR4","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature Vol. 521, No. 7553, 436\u2013444, 2015.","journal-title":"Nature"},{"key":"209_CR5","unstructured":"Goodfellow, I.; Bengio, Y.; Courville, A.; Bengio, Y. Deep Learning. MIT Press, 2016."},{"issue":"2","key":"209_CR6","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1111\/cgf.12592","volume":"34","author":"M Zwicker","year":"2015","unstructured":"Zwicker, M.; Jarosz, W.; Lehtinen, J.; Moon, B.; Ramamoorthi, R.; Rousselle, F.; Sen, P.; Soler, C.; Yoon, S.-E. Recent advances in adaptive sampling and reconstruction for Monte Carlo rendering. Computer Graphics Forum Vol. 34, No. 2, 667\u2013681, 2015.","journal-title":"Computer Graphics Forum"},{"key":"209_CR7","doi-asserted-by":"crossref","unstructured":"Dahlberg, H.; Adler, D.; Newlin, J. Machine-learning denoising in feature film production. In: Proceedings of the ACM SIGGRAPH 2019 Talks, Article No. 21, 2019.","DOI":"10.1145\/3306307.3328150"},{"key":"209_CR8","doi-asserted-by":"crossref","unstructured":"Chaitanya, C. R. A.; Kaplanyan, A. S.; Schied, C.; Salvi, M.; Lefohn, A.; Nowrouzezahrai, D.; Aila, T. Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder. ACM Transactions on Graphics Vol. 36, No. 4, Article No. 98, 2017.","DOI":"10.1145\/3072959.3073601"},{"key":"209_CR9","doi-asserted-by":"crossref","unstructured":"Vogels, T.; Rousselle, F.; McWilliams, B.; R\u00f6thlin, G.; Harvill, A.; Adler, D.; Meyer, M.; Nov\u00e1k, J. Denoising with kernel prediction and asymmetric loss functions. ACM Transactions on Graphics Vol. 37, No. 4, Article No. 124, 2018.","DOI":"10.1145\/3197517.3201388"},{"key":"209_CR10","doi-asserted-by":"crossref","unstructured":"Kalantari, N. K.; Bako, S.; Sen, P. A machine learning approach for filtering Monte Carlo noise. ACM Transactions on Graphics Vol. 34, No. 4, Article No. 122, 2015.","DOI":"10.1145\/2766977"},{"key":"209_CR11","unstructured":"Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer Science & Business Media, 2009."},{"key":"209_CR12","series-title":"Technical Report","doi-asserted-by":"publisher","DOI":"10.21236\/AD0256582","volume-title":"Principles of neurodynamics. perceptrons and the theory of brain mechanisms","author":"F Rosenblatt","year":"1961","unstructured":"Rosenblatt, F. Principles of neurodynamics. perceptrons and the theory of brain mechanisms. Technical Report. Cornell Aeronautical Lab Inc Buffalo NY, 1961."},{"key":"209_CR13","doi-asserted-by":"crossref","unstructured":"Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. Learning internal representations by error propagation. Technical Report. California Univ San Diego La Jolla Inst for Cognitive Science, 1985.","DOI":"10.21236\/ADA164453"},{"key":"209_CR14","doi-asserted-by":"publisher","first-page":"116336","DOI":"10.1109\/ACCESS.2020.2999891","volume":"8","author":"Q W Xing","year":"2020","unstructured":"Xing, Q. W.; Chen, C. Y. Path tracing denoising based on SURE adaptive sampling and neural network. IEEE Access Vol. 8, 116336\u2013116349, 2020.","journal-title":"IEEE Access"},{"issue":"6","key":"209_CR15","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1214\/aos\/1176345632","volume":"9","author":"C M Stein","year":"1981","unstructured":"Stein, C. M. Estimation of the mean of a multivariate normal distribution. The Annals of Statistics Vol. 9, No. 6, 1135\u20131151, 1981.","journal-title":"The Annals of Statistics"},{"key":"209_CR16","doi-asserted-by":"crossref","unstructured":"Bako, S.; Vogels, T.; McWilliams, B.; Meyer, M.; Nov\u00e1K, J.; Harvill, A.; Sen, P.; Derose, T.; Rousselle, F. Kernel-predicting convolutional networks for denoising Monte Carlo renderings. ACM Transactions on Graphics Vol. 36, No. 4, Article No. 97, 2017.","DOI":"10.1145\/3072959.3073708"},{"key":"209_CR17","doi-asserted-by":"crossref","unstructured":"Gharbi, M.; Li, T.-M.; Aittala, M.; Lehtinen, J.; Durand, F. Sample-based Monte Carlo denoising using a kernel-splatting network. ACM Transactions on Graphics Vol. 38, No. 4, Article No. 125, 2019.","DOI":"10.1145\/3306346.3322954"},{"issue":"4","key":"209_CR18","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y.; Boser, B.; Denker, J. S.; Henderson, D.; Howard, R. E.; Hubbard, W.; Jackel, L. D. Backpropagation applied to handwritten zip code recognition. Neural Computation Vol. 1, No. 4, 541\u2013551, 1989.","journal-title":"Neural Computation"},{"key":"209_CR19","unstructured":"LeCun, Y.; Boser, B. E.; Denker, J. S.; Henderson, D.; Howard, R. E.; Hubbard, W. E.; Jackel, L. D. Handwritten digit recognition with a back-propagation network. In: Proceedings of the 2nd International Conference on Neural Information Processing Systems, 396\u2013404, 1989."},{"key":"209_CR20","doi-asserted-by":"crossref","unstructured":"Back, J.; Hua, B.-S.; Hachisuka, T.; Moon, B. Deep combiner for independent and correlated pixel estimates. ACM Transactions on Graphics Vol. 39, No. 6, Article No. 242, 2020.","DOI":"10.1145\/3414685.3417847"},{"key":"209_CR21","doi-asserted-by":"crossref","unstructured":"Xu, B.; Zhang, J. F.; Wang, R.; Xu, K.; Yang, Y. L.; Li, C.; Tang, R. Adversarial Monte Carlo denoising with conditioned auxiliary feature modulation. ACM Transactions on Graphics Vol. 38, No. 6, Article No. 224, 2019.","DOI":"10.1145\/3355089.3356547"},{"key":"209_CR22","first-page":"2672","volume":"2","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y. Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, Vol. 2, 2672\u20132680, 2014.","journal-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems"},{"issue":"1","key":"209_CR23","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell, A.; White, T.; Dumoulin, V.; Arulkumaran, K.; Sengupta, B.; Bharath, A. A. Generative adversarial networks: An overview. IEEE Signal Processing Magazine Vol. 35, No. 1, 53\u201365, 2018.","journal-title":"IEEE Signal Processing Magazine"},{"key":"209_CR24","doi-asserted-by":"crossref","unstructured":"Alsaiari, A.; Rustagi, R.; Thomas, M. M.; Forbes, A. G. Image denoising using a generative adversarial network. In: Proceedings of the IEEE 2nd International Conference on Information and Computer Technologies, 126\u2013132, 2019.","DOI":"10.1109\/INFOCT.2019.8710893"},{"key":"209_CR25","doi-asserted-by":"crossref","unstructured":"He, K. M.; Zhang, X. Y.; Ren, S. Q.; Sun, J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770\u2013778, 2016.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"209_CR26","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s41095-019-0142-3","volume":"5","author":"K M Wong","year":"2019","unstructured":"Wong, K. M.; Wong, T. T. Deep residual learning for denoising Monte Carlo renderings. Computational Visual Media Vol. 5, No. 3, 239\u2013255, 2019.","journal-title":"Computational Visual Media"},{"key":"209_CR27","doi-asserted-by":"publisher","first-page":"21177","DOI":"10.1109\/ACCESS.2018.2886005","volume":"7","author":"X Yang","year":"2019","unstructured":"Yang, X.; Wang, D. W.; Hu, W. B.; Zhao, L. J.; Piao, X. L.; Zhou, D. S.; Zhang, Q.; Yin, B.; Cai, Q.; Wei, X. Fast reconstruction for Monte Carlo rendering using deep convolutional networks. IEEE Access Vol. 7, 21177\u201321187, 2019.","journal-title":"IEEE Access"},{"issue":"5","key":"209_CR28","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1007\/s11390-019-1964-2","volume":"34","author":"X Yang","year":"2019","unstructured":"Yang, X.; Wang, D. W.; Hu, W. B.; Zhao, L. J.; Yin, B. C.; Zhang, Q.; Wei, X.-P.; Fu, H. DEMC: A deep dual-encoder network for denoising Monte Carlo rendering. Journal of Computer Science and Technology Vol. 34, No. 5, 1123\u20131135, 2019.","journal-title":"Journal of Computer Science and Technology"},{"key":"209_CR29","first-page":"279","volume":"1","author":"D H Ballard","year":"1987","unstructured":"Ballard, D. H. Modular learning in neural networks. In: Proceedings of the 6th National Conference on Artificial Intelligence, Vol. 1, 279\u2013284, 1987.","journal-title":"Proceedings of the 6th National Conference on Artificial Intelligence"},{"key":"209_CR30","doi-asserted-by":"crossref","unstructured":"Vincent, P.; Larochelle, H.; Bengio, Y.; Manzagol, P. A. Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, 1096\u20131103, 2008.","DOI":"10.1145\/1390156.1390294"},{"issue":"4","key":"209_CR31","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1111\/cgf.13473","volume":"37","author":"A Kuznetsov","year":"2018","unstructured":"Kuznetsov, A.; Kalantari, N. K.; Ramamoorthi, R. Deep adaptive sampling for low sample count rendering. Computer Graphics Forum Vol. 37, No. 4, 35\u201344, 2018.","journal-title":"Computer Graphics Forum"},{"issue":"4","key":"209_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/cgf.14049","volume":"39","author":"J Munkberg","year":"2020","unstructured":"Munkberg, J.; Hasselgren, J. Neural denoising with layer embeddings. Computer Graphics Forum Vol. 39, No. 4, 1\u201312, 2020.","journal-title":"Computer Graphics Forum"},{"issue":"4","key":"209_CR33","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1111\/cgf.12681","volume":"34","author":"J Hanika","year":"2015","unstructured":"Hanika, J.; Droske, M.; Fascione, L. Manifold next event estimation. Computer Graphics Forum Vol. 34, No. 4, 87\u201397, 2015.","journal-title":"Computer Graphics Forum"},{"key":"209_CR34","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1111\/cgf.14194","volume":"40","author":"W H Lin","year":"2021","unstructured":"Lin, W. H.; Wang, B. B.; Yang, J.; Wang, L.; Yan, L. Q. Path-based Monte Carlo denoising using a three-scale neural network. Computer Graphics Forum Vol. 40, 369\u2013381, 2021.","journal-title":"Computer Graphics Forum"},{"key":"209_CR35","doi-asserted-by":"crossref","unstructured":"Levoy, M.; Hanrahan, P. Light field rendering. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, 31\u201342, 1996.","DOI":"10.1145\/237170.237199"},{"issue":"2","key":"209_CR36","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s41095-020-0167-7","volume":"6","author":"W H Lin","year":"2020","unstructured":"Lin, W. H.; Wang, B. B.; Wang, L.; Holzschuch, N. A detail preserving neural network model for Monte Carlo denoising. Computational Visual Media Vol. 6, No. 2, 157\u2013168, 2020.","journal-title":"Computational Visual Media"},{"issue":"3","key":"209_CR37","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1145\/1073204.1073320","volume":"24","author":"F Durand","year":"2005","unstructured":"Durand, F.; Holzschuch, N.; Soler, C.; Chan, E.; Sillion, F. X. A frequency analysis of light transport. ACM Transactions on Graphics Vol. 24, No. 3, 1115\u20131126, 2005.","journal-title":"ACM Transactions on Graphics"},{"key":"209_CR38","doi-asserted-by":"crossref","unstructured":"Belcour, L.; Soler, C.; Subr, K.; Holzschuch, N.; Durand, F. 5D Covariance tracing for efficient defocus and motion blur. ACM Transactions on Graphics Vol. 32, No. 3, Article No. 31, 2013.","DOI":"10.1145\/2487228.2487239"},{"issue":"10","key":"209_CR39","doi-asserted-by":"publisher","first-page":"2961","DOI":"10.1109\/TVCG.2019.2909875","volume":"26","author":"Y L Liang","year":"2020","unstructured":"Liang, Y. L.; Wang, B. B.; Wang, L.; Holzschuch, N. Fast computation of single scattering in participating media with refractive boundaries using frequency analysis. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 10, 2961\u20132969, 2020.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"issue":"7","key":"209_CR40","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1111\/cgf.13858","volume":"38","author":"S Bako","year":"2019","unstructured":"Bako, S.; Meyer, M.; DeRose, T.; Sen, P. Offline deep importance sampling for Monte Carlo path tracing. Computer Graphics Forum Vol. 38, No. 7, 527\u2013542, 2019.","journal-title":"Computer Graphics Forum"},{"key":"209_CR41","doi-asserted-by":"crossref","unstructured":"Huo, Y.; Wang, R.; Zheng, R.; Xu, H.; Bao, H.; Yoon, S.-E. Adaptive incident radiance field sampling and reconstruction using deep reinforcement learning. ACM Transactions on Graphics Vol. 39, No. 1, Article No. 6, 2020.","DOI":"10.1145\/3368313"},{"key":"209_CR42","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.cag.2020.09.007","volume":"94","author":"G Jiang","year":"2021","unstructured":"Jiang, G.; Kainz, B. Deep radiance caching: Convolutional autoencoders deeper in ray tracing. Computers & Graphics Vol. 94, 22\u201331, 2021.","journal-title":"Computers & Graphics"},{"key":"209_CR43","doi-asserted-by":"crossref","unstructured":"Lehtinen, J.; Karras, T.; Laine, S.; Aittala, M.; Durand, F.; Aila, T. Gradient-domain metropolis light transport. ACM Transactions on Graphics Vol. 32, No. 4, Article No. 95, 2013.","DOI":"10.1145\/2461912.2461943"},{"key":"209_CR44","doi-asserted-by":"crossref","unstructured":"Kettunen, M.; Manzi, M.; Aittala, M.; Lehtinen, J.; Durand, F.; Zwicker, M. Gradient-domain path tracing. ACM Transactions on Graphics Vol. 34, No. 4, Article No. 123, 2015.","DOI":"10.1145\/2766997"},{"issue":"2","key":"209_CR45","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1111\/cgf.13652","volume":"38","author":"B S Hua","year":"2019","unstructured":"Hua, B. S.; Gruson, A.; Petitjean, V.; Zwicker, M.; Nowrouzezahrai, D.; Eisemann, E.; Hachisuka, T. A survey on gradient-domain rendering. Computer Graphics Forum Vol. 38, No. 2, 455\u2013472, 2019.","journal-title":"Computer Graphics Forum"},{"key":"209_CR46","doi-asserted-by":"crossref","unstructured":"Kettunen, M.; H\u00e4rk\u00f6nen, E.; Lehtinen, J. Deep convolutional reconstruction for gradient-domain rendering. ACM Transactions on Graphics Vol. 38, No. 4, Article No. 126, 2019.","DOI":"10.1145\/3306346.3323038"},{"key":"209_CR47","doi-asserted-by":"crossref","unstructured":"Guo, J.; Li, M.; Li, Q.; Qiang, Y.; Hu, B.; Guo, Y.; Yan, L.-Q. GradNet: Unsupervised deep screened poisson reconstruction for gradient-domain rendering. ACM Transactions on Graphics Vol. 38, No. 6, Article No. 223, 2019.","DOI":"10.1145\/3355089.3356538"},{"key":"209_CR48","doi-asserted-by":"crossref","unstructured":"Jensen, H. W. Realistic Image Synthesis Using Photon Mapping. AK Peters\/CRC Press, 2001.","DOI":"10.1201\/b10685"},{"issue":"3","key":"209_CR49","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1631\/FITEE.1500251","volume":"17","author":"C M Kang","year":"2016","unstructured":"Kang, C. M.; Wang, L.; Xu, Y. N.; Meng, X. X. A survey of photon mapping state-of-the-art research and future challenges. Frontiers of Information Technology & Electronic Engineering Vol. 17, No. 3, 185\u2013199, 2016.","journal-title":"Frontiers of Information Technology & Electronic Engineering"},{"issue":"4","key":"209_CR50","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1111\/cgf.14052","volume":"39","author":"S Zhu","year":"2020","unstructured":"Zhu, S.; Xu, Z.; Jensen, H. W.; Su, H.; Ramamoorthi, R. Deep kernel density estimation for photon mapping. Computer Graphics Forum Vol. 39, No. 4, 35\u201345, 2020.","journal-title":"Computer Graphics Forum"},{"key":"209_CR51","doi-asserted-by":"crossref","unstructured":"Hachisuka, T.; Ogaki, S.; Jensen, H. W. Progressive photon mapping. In: Proceedings of the ACM SIGGRAPH Asia 2008 papers, Article No. 130, 2008.","DOI":"10.1145\/1457515.1409083"},{"issue":"3","key":"209_CR52","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1007\/s11390-020-0264-1","volume":"35","author":"Z Zeng","year":"2020","unstructured":"Zeng, Z.; Wang, L.; Wang, B. B.; Kang, C. M.; Xu, Y. N. Denoising stochastic progressive photon mapping renderings using a multi-residual network. Journal of Computer Science and Technology Vol. 35, No. 3, 506\u2013521, 2020.","journal-title":"Journal of Computer Science and Technology"},{"issue":"6088","key":"209_CR53","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"D E Rumelhart","year":"1986","unstructured":"Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. Learning representations by back-propagating errors. Nature Vol. 323, No. 6088, 533\u2013536, 1986.","journal-title":"Nature"},{"key":"209_CR54","first-page":"235","volume":"1","author":"Y Huang","year":"2015","unstructured":"Huang, Y.; Wang, W.; Wang, L. Bidirectional recurrent convolutional networks for multi-frame super-resolution. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, Vol. 1, 235\u2013243, 2015.","journal-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems"},{"key":"209_CR55","doi-asserted-by":"crossref","unstructured":"Mehta, S. U.; Wang, B.; Ramamoorthi, R. Axis-aligned filtering for interactive sampled soft shadows. ACM Transactions on Graphics Vol. 31, No. 6, Article No. 163, 2012.","DOI":"10.1145\/2366145.2366182"},{"key":"209_CR56","unstructured":"Dammertz, H.; Sewtz, D.; Hanika, J.; Lensch, H. P. A. Edge-avoiding \u00c0-Trous wavelet transform for fast global illumination filtering. In: Proceedings of the Conference on High Performance Graphics, 67\u201375, 2010."},{"key":"209_CR57","doi-asserted-by":"crossref","unstructured":"Li, T. M.; Wu, Y. T.; Chuang, Y. Y. SURE-based optimization for adaptive sampling and reconstruction. ACM Transactions on Graphics Vol. 31, No. 6, Article No. 194, 2012.","DOI":"10.1145\/2366145.2366213"},{"issue":"2","key":"209_CR58","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1111\/cgf.13919","volume":"39","author":"J Hasselgren","year":"2020","unstructured":"Hasselgren, J.; Munkberg, J.; Salvi, M.; Patney, A.; Lefohn, A. Neural temporal adaptive sampling and denoising. Computer Graphics Forum Vol. 39, No. 2, 147\u2013155, 2020.","journal-title":"Computer Graphics Forum"},{"key":"209_CR59","unstructured":"Meng, X.; Zheng, Q.; Varshney, A.; Singh, G.; Zwicker, M. Real-time Monte Carlo denoising with the neural bilateral grid. In: Proceedings of the Eurographics Symposium on Rendering, 2020."},{"issue":"4","key":"209_CR60","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1145\/378456.378484","volume":"22","author":"R A Drebin","year":"1988","unstructured":"Drebin, R. A.; Carpenter, L.; Hanrahan, P. Volume rendering. ACM SIGGRAPH Computer Graphics Vol. 22, No. 4, 65\u201374, 1988.","journal-title":"ACM SIGGRAPH Computer Graphics"},{"issue":"2","key":"209_CR61","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/2945.468400","volume":"1","author":"N Max","year":"1995","unstructured":"Max, N. Optical models for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics Vol. 1, No. 2, 99\u2013108, 1995.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"209_CR62","doi-asserted-by":"crossref","unstructured":"Kallweit, S.; M\u00fcller, T.; McWilliams, B.; Gross, M.; Nov\u00e1k, J. Deep scattering: Rendering atmospheric clouds with radiance-predicting neural networks. ACM Transactions on Graphics Vol. 36, No. 6, Article No. 231, 2017.","DOI":"10.1145\/3130800.3130880"},{"key":"209_CR63","unstructured":"Panin, M.; Nikolenko, S. Faster RPNN: Rendering clouds with latent space light probes. In: Proceedings of the SIGGRAPH Asia 2019 Technical Briefs, 21\u201324, 2019."},{"issue":"7","key":"209_CR64","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1111\/cgf.14137","volume":"39","author":"Z L Xu","year":"2020","unstructured":"Xu, Z. L.; Sun, Q.; Wang, L.; Xu, Y. N.; Wang, B. B. Unsupervised image reconstruction for gradient-domain volumetric rendering. Computer Graphics Forum Vol. 39, No. 7, 193\u2013203, 2020.","journal-title":"Computer Graphics Forum"},{"key":"209_CR65","doi-asserted-by":"crossref","unstructured":"Hofmann, N.; Martschinke, J.; Engel, K.; Stamminger, M. Neural denoising for path tracing of medical volumetric data. In: Proceedings of the ACM on Computer Graphics and Interactive Techniques, Article No. 13, 2020.","DOI":"10.1145\/3406181"},{"key":"209_CR66","first-page":"326","volume-title":"Rendering Techniques\u2019 95","author":"H W Jensen","year":"1995","unstructured":"Jensen, H. W. Importance driven path tracing using the photon map. In: Rendering Techniques\u2019 95. Hanrahan, P. M.; Purgathofer, W. Eds. Springer Vienna, 326\u2013335, 1995."},{"key":"209_CR67","doi-asserted-by":"crossref","unstructured":"Hey, H.; Purgathofer, W. Importance sampling with hemispherical particle footprints. In: Proceedings of the 18th Spring Conference on Computer Graphics, 107\u2013114, 2002.","DOI":"10.1145\/584458.584476"},{"issue":"7540","key":"209_CR68","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G. et al. Human-level control through deep reinforcement learning. Nature Vol. 518, No. 7540, 529\u2013533, 2015.","journal-title":"Nature"},{"issue":"7587","key":"209_CR69","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D.; Huang, A.; Maddison, C. J.; Guez, A.; Sifre, L.; van den Driessche, G.; Schrittwieser, J.; Antonoglou, I.; Panneershelvam, V.; Lanctot, M. et al. Mastering the game of Go with deep neural networks and tree search. Nature Vol. 529, No. 7587, 484\u2013489, 2016.","journal-title":"Nature"},{"key":"209_CR70","unstructured":"Nvidia. Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder. 2020. Available at https:\/\/research.nvidia.com\/publication\/interactive-reconstruction-monte-carlo-image-sequences-using-recurrent-denoising."}],"container-title":["Computational Visual Media"],"original-title":[],"link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41095-021-0209-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41095-021-0209-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41095-021-0209-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10750449\/10897551\/10897554.pdf?arnumber=10897554","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T18:38:40Z","timestamp":1762367920000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10897554\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6]]},"references-count":70,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1007\/s41095-021-0209-9","relation":{},"ISSN":["2096-0662","2096-0433"],"issn-type":[{"value":"2096-0662","type":"electronic"},{"value":"2096-0433","type":"print"}],"subject":[],"published":{"date-parts":[[2021,6]]},"assertion":[{"value":"25 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}