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Past works differ substantially in their experimental overhead, from single\u2010 or few\u2010image approaches, that employ significant (often learned) priors at the expense of biased reconstructions, to more accurate approaches that tend to be time\u2010consuming, which to a good part is due to the need for carefully controlled illumination. Moreover, as we will show, the frequently employed point\u2010light or directional lighting tends to clip highlights and under\u2010sample the reflectance of glossy surfaces, leading to incorrect reconstructions under previously unseen illumination. Our work aims to strike a new balance, combining a low\u2010overhead capture methodology with a fast (neural) model fit. A key feature of our approach is the use of handheld, indirect bounce light that enables a convenient capture methodology, limits the dynamic range of the reflectance (effectively avoiding highlight clipping) and ensures contiguous hemispherical incidence, even with few images, eliminating under\u2010sampling of highly specular reflectance lobes. Moreover, our approach does not require training on pre\u2010existing material datasets and thus is not restricted by the choice of dataset, and its inference scales linearly with the number of pixels, scaling exceptionally well to large image sizes. As a result, our method enables high\u2010resolution capture of a spatially\u2010varying reflectance distribution function (SVBRDF) from a small set of casually captured, indirectly lit photographs, making high\u2010quality material acquisition practical even on consumer hardware. Overall, we believe that our method occupies a unique trade\u2010off between acquisition effort, model assumptions and resulting quality, and it has the potential to transform areas that routinely use handheld point\u2010light sources, such as the popular reflectance transformation imaging (RTI), leading to more faithful reproductions of artefacts and their surface characteristics.<\/jats:p>","DOI":"10.1111\/cgf.70359","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T15:14:37Z","timestamp":1777562077000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["High\u2010Gloss SVBRDF Capture Using Bounce Light"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3013-8994","authenticated-orcid":false,"given":"Tom\u00e1\u0161","family":"Iser","sequence":"first","affiliation":[{"name":"Friedrich\u2010Alexander\u2010Universit\u00e4t Erlangen\u2010N\u00fcrnberg  Germany"},{"name":"Charles University, Faculty of Mathematics and Physics  Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9317-5149","authenticated-orcid":false,"given":"Andrei\u2010Timotei","family":"Ardelean","sequence":"additional","affiliation":[{"name":"Friedrich\u2010Alexander\u2010Universit\u00e4t Erlangen\u2010N\u00fcrnberg  Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4322-8844","authenticated-orcid":false,"given":"Tim","family":"Weyrich","sequence":"additional","affiliation":[{"name":"Friedrich\u2010Alexander\u2010Universit\u00e4t Erlangen\u2010N\u00fcrnberg  Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925917"},{"key":"e_1_2_9_2_3","doi-asserted-by":"crossref","unstructured":"doi:10.1145\/2897824.2925917. 2 3","DOI":"10.1145\/2897824.2925917"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/2461912.2461978"},{"key":"e_1_2_9_3_3","doi-asserted-by":"crossref","unstructured":"doi:10.1145\/2461912.2461978. 3","DOI":"10.1145\/2461912.2461978"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2766967"},{"key":"e_1_2_9_4_3","doi-asserted-by":"crossref","unstructured":"doi:10.1145\/2766967. 2 3","DOI":"10.1145\/2766967"},{"key":"e_1_2_9_5_2","unstructured":"BossM. 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