{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T02:40:10Z","timestamp":1774924810529,"version":"3.50.1"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T00:00:00Z","timestamp":1597190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>Scattering from specular surfaces produces complex optical effects that are frequently encountered in realistic scenes: intricate caustics due to focused reflection, multiple refraction, and high-frequency glints from specular microstructure. Yet, despite their importance and considerable research to this end, sampling of light paths that cause these effects remains a formidable challenge.<\/jats:p>\n          <jats:p>In this article, we propose a surprisingly simple and general sampling strategy for specular light paths including the above examples, unifying the previously disjoint areas of caustic and glint rendering into a single framework. Given two path vertices, our algorithm stochastically finds a specular subpath connecting the endpoints. In contrast to prior work, our method supports high-frequency normal- or displacement-mapped geometry, samples specular-diffuse-specular (\"SDS\") paths, and is compatible with standard Monte Carlo methods including unidirectional path tracing. Both unbiased and biased variants of our approach can be constructed, the latter often significantly reducing variance, which may be appealing in applied settings (e.g. visual effects). We demonstrate our method on a range of challenging scenes and evaluate it against state-of-the-art methods for rendering caustics and glints.<\/jats:p>","DOI":"10.1145\/3386569.3392408","type":"journal-article","created":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T11:44:27Z","timestamp":1597232667000},"update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["Specular manifold sampling for rendering high-frequency caustics and glints"],"prefix":"10.1145","volume":"39","author":[{"given":"Tizian","family":"Zeltner","sequence":"first","affiliation":[{"name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Iliyan","family":"Georgiev","sequence":"additional","affiliation":[{"name":"Autodesk, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzel","family":"Jakob","sequence":"additional","affiliation":[{"name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8,12]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.13182\/NSE07-A2707"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3214745.3214751"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275058"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366211"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1618452.1618487"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366210"},{"key":"e_1_2_2_7_1","volume-title":"Manifold Next Event Estimation. 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Proceedings of Third International Conference on Computational Graphics and Visualization Techniques (Compugraphics' 93 (01 1993)."},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461912.2461943"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/142920.134082"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13227"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356498"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1730804.1730834"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818119"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275030"},{"key":"e_1_2_2_28_1","unstructured":"Sebastien Speierer Christophe Hery Ryusuke Villemin and Wenzel Jakob. 2018. Caustic Connection Strategies for Bidirectional Path Tracing. Pixar Technical Memo #18-01."},{"key":"e_1_2_2_29_1","unstructured":"Eric Veach. 1998. Robust Monte Carlo Methods for Light Transport Simulation. Ph.D. Dissertation. Stanford CA USA. Advisor(s) Guibas Leonidas J. AAI9837162."},{"key":"e_1_2_2_30_1","volume-title":"Photorealistic Rendering Techniques","author":"Veach Eric","unstructured":"Eric Veach and Leonidas Guibas. 1995a. Bidirectional estimators for light transport. In Photorealistic Rendering Techniques. Springer, 145--167."},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/218380.218498"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601203"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.2312\/EGWR\/EGSR07\/195-206"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/1531326.1531398"},{"key":"e_1_2_2_35_1","unstructured":"Beibei Wang Milo\u0161 Ha\u0161an Nicolas Holzschuch and Ling-Qi Yan. 2019. Example-Based Microstructure Rendering with Constant Storage. Technical Report 2019-08. 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