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DTSTAMP:20230103T035307Z
LOCATION:Auditorium\, Level 5\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T100000
DTEND;TZID=Asia/Seoul:20221206T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_235@linklings.com
SUMMARY:Lightweight Neural Basis Functions for All-Frequency Shading
DESCRIPTION:Technical Papers\n\nLightweight Neural Basis Functions for All
 -Frequency Shading\n\nXu, Zeng, Wu, Wang, Yan\n\nBasis functions provide b
 oth the abilities for compact representation and the properties for effici
 ent computation. Therefore, they are pervasively used in rendering to perf
 orm all-frequency shading. However, common basis functions, including sphe
 rical harmonics (SH), wavelets, and spherical Gaussians (SG) all have thei
 r own limitations, such as low-frequency for SH, not rotationally invarian
 t for wavelets, and no multiple product support for SG. In this paper, we 
 present \empf{neural basis functions}, an implicit and data-driven set of 
 basis functions that circumvents the limitations with all desired properti
 es. We first introduce a representation neural network that takes any gene
 ral 2D spherical function (e.g. environment lighting, BRDF, and visibility
 ) as input and projects it onto the latent space as coefficients of our ne
 ural basis functions. Then, we design several lightweight neural networks 
 that perform different types of computation, giving our basis functions di
 fferent computational properties such as double/triple product integrals a
 nd rotations. We demonstrate the practicality of our neural basis function
 s by integrating them into all-frequency shading applications, showing tha
 t our method not only achieves a compression rate of $0.39\%$ and $10\time
 s$-$40\times$ better performance than wavelets at equal quality, but also 
 renders all-frequency lighting effects in real-time without the aforementi
 oned limitations from classic basis functions.\n\nRegistration Category: F
 ULL ACCESS, EXPERIENCE PLUS ACCESS, EXPERIENCE ACCESS, TRADE EXHIBITOR\n\n
 Language: ENGLISH\n\nFormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_235&sess=sess15
 3
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