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Graph."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>\n            Complex luminaires, such as grand chandeliers, can be extremely costly to render because the light-emitting sources are typically encased in complex refractive geometry, creating difficult light paths that require many samples to evaluate with Monte Carlo approaches. Previous work has attempted to speed up this process, but the methods are either inaccurate, require the storage of very large lightfields, and\/or do not fit well into modern path-tracing frameworks. Inspired by the success of deep networks, which can model complex relationships robustly and be evaluated efficiently, we propose to use a machine learning framework to compress a complex luminaire's lightfield into an implicit neural representation. Our approach can easily plug into conventional renderers, as it works with the standard techniques of path tracing and multiple importance sampling (MIS). Our solution is to train three networks to perform the essential operations for\n            <jats:italic>evaluating<\/jats:italic>\n            the complex luminaire at a specific point and view direction,\n            <jats:italic>importance sampling<\/jats:italic>\n            a point on the luminaire given a shading location, and\n            <jats:italic>blending<\/jats:italic>\n            to determine the transparency of luminaire queries to properly composite them with other scene elements. We perform favorably relative to state-of-the-art approaches and render final images that are close to the high-sample-count reference with only a fraction of the computation and storage costs, with no need to store the original luminaire geometry and materials.\n          <\/jats:p>","DOI":"10.1145\/3450626.3459798","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:04:26Z","timestamp":1626739466000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Neural complex luminaires"],"prefix":"10.1145","volume":"40","author":[{"given":"Junqiu","family":"Zhu","sequence":"first","affiliation":[{"name":"Shandong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaoyi","family":"Bai","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zilin","family":"Xu","sequence":"additional","affiliation":[{"name":"Shandong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steve","family":"Bako","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edgar","family":"Vel\u00e1zquez-Armend\u00e1riz","sequence":"additional","affiliation":[{"name":"Pure Storage"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Wang","sequence":"additional","affiliation":[{"name":"Shandong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pradeep","family":"Sen","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Milo\u0161","family":"Ha\u0161an","sequence":"additional","affiliation":[{"name":"Adobe Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling-Qi","family":"Yan","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"Ian Ashdown. 1995. 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Chaitanya , Anton Kaplanyan , Christoph Schied , Marco Salvi , Aaron Lefohn , Derek Nowrouzezahrai , and Timo Aila . 2017. Interactive Reconstruction of Noisy Monte Carlo Image Sequences using a Recurrent Autoencoder. ACM Transactions on Graphics (July 2017 ). Chakravarty R. A. Chaitanya, Anton Kaplanyan, Christoph Schied, Marco Salvi, Aaron Lefohn, Derek Nowrouzezahrai, and Timo Aila. 2017. Interactive Reconstruction of Noisy Monte Carlo Image Sequences using a Recurrent Autoencoder. ACM Transactions on Graphics (July 2017)."},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.509883"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2005.863954"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/566654.566601"},{"key":"e_1_2_2_10_1","unstructured":"Thomas Davies Derek Nowrouzezahrai and Alec Jacobson. 2020. 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