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However, many applications of interest in computer graphics and computational optics demand a more precise understanding of light: as waves. For example, accurately modelling scattering effects like diffraction or interference requires a model that provides the coherence of light waves arriving at surfaces. While recent work in Physical Light Transport [Steinberg et al. 2022; Steinberg and Yan 2021] has introduced such a model, it requires tracing light paths starting from the light sources, which is often less efficient than tracing them from the sensor, and does not allow the use of many effective importance sampling techniques.<\/jats:p>\n          <jats:p>\n            We introduce a new model for wave optical light transport that is based on the fact that sensors aggregate the measurement of many light waves when capturing an image. This allows us to compactly represent the statistics of light waves in a\n            <jats:italic>generalized ray.<\/jats:italic>\n            Generalized rays allow sampling light paths starting from the sensor and applying sophisticated path tracing sampling techniques while still accurately modelling the wave nature of light. Our model is computationally efficient and straightforward to add to an existing path tracer; this offers the prospect of wave optics becoming the foundation of most renderers in the future. Using our model, we show that it is possible to render complex scenes under wave optics with high performance, which has not been possible with any existing method.\n          <\/jats:p>","DOI":"10.1145\/3687902","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T15:46:04Z","timestamp":1732031164000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A Generalized Ray Formulation For Wave-Optical Light Transport"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2748-4036","authenticated-orcid":false,"given":"Shlomi","family":"Steinberg","sequence":"first","affiliation":[{"name":"University of Waterloo, Waterloo, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3993-5789","authenticated-orcid":false,"given":"Ravi","family":"Ramamoorthi","sequence":"additional","affiliation":[{"name":"NVIDIA, San Francisco, United States of America"},{"name":"University of California San Diego, San Francisco, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8799-7119","authenticated-orcid":false,"given":"Benedikt","family":"Bitterli","sequence":"additional","affiliation":[{"name":"NVIDIA, Redmond, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3761-2989","authenticated-orcid":false,"given":"Eugene","family":"d'Eon","sequence":"additional","affiliation":[{"name":"NVIDIA, Wellington, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9379-094X","authenticated-orcid":false,"given":"Ling-Qi","family":"Yan","sequence":"additional","affiliation":[{"name":"University of California Santa Barbara, Santa Barbara, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0566-8291","authenticated-orcid":false,"given":"Matt","family":"Pharr","sequence":"additional","affiliation":[{"name":"NVIDIA, San Francisco, United States of America"}]}],"member":"320","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1364\/josaa.32.001403"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073620"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rsn.2019.0070"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.23919\/EuCAP48036.2020.9135577"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2014.2317803"},{"key":"e_1_2_1_6_1","first-page":"2","article-title":"RTX on---The NVIDIA Turing GPU","volume":"40","author":"Burgess John","year":"2020","unstructured":"John Burgess. 2020. 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