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DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241205T130000
DTEND;TZID=Asia/Tokyo:20241205T141000
UID:siggraphasia_SIGGRAPH Asia 2024_sess131@linklings.com
SUMMARY:Sampling and Light Transport
DESCRIPTION:Technical Papers\n\nEach Paper gives a 10 minute presentation.
 \n\nNeural Product Importance Sampling via Warp Composition\n\nAchieving h
 igh efficiency in modern photorealistic rendering methods hinges on using 
 Monte Carlo sampling distributions that closely approximate the illuminati
 on integral estimated for every pixel. Samples are typically generated fro
 m a set of simple distributions, each targeting a different factor ...\n\n
 \nJoey Litalien (McGill University) and Miloš Hašan, Fujun Luan, Krishna M
 ullia, and Iliyan Georgiev (Adobe Research)\n---------------------\nBSDF i
 mportance sampling using a diffusion model\n\nMany real-world materials fe
 ature complex BSDFs (Bidirectional Scattering Distribution Functions) that
  require significant storage, making the use of neural networks to represe
 nt BSDFs appealing. Previous neural sampling methods, primarily using anal
 ytical lobe mixtures and normalizing flows, often ...\n\n\nZiyang Fu, Yash
  Belhe, Haolin Lu, Liwen Wu, Bing Xu, and Tzu-Mao Li (University of Califo
 rnia San Diego)\n---------------------\nManifold Sampling for Differentiab
 le Uncertainty in Radiance Fields\n\nRadiance fields are powerful and, hen
 ce, popular models for representing the appearance of complex scenes. Yet,
  constructing them based on image observations gives rise to ambiguities a
 nd uncertainties. We propose a versatile approach for learning Gaussian ra
 diance fields with explicit and fine-grai...\n\n\nLinjie Lyu (Max Planck I
 nstitute for Informatics), Ayush Tewari (MIT CSAIL), Marc Habermann (Max P
 lanck Institute for Informatics), Shunsuke Saito and Michael Zollhöfer (Me
 ta Codec Avatars Lab), and Thomas Leimkühler and Christian Theobalt (Max P
 lanck Institute for Informatics)\n---------------------\nHierarchical Ligh
 t Sampling with Accurate Spherical Gaussian Lighting\n\nImportance samplin
 g using a light tree (i.e., a hierarchy of light clusters) has been widely
  used for many-light rendering. This technique samples a light source by s
 tochastically traversing the tree according to the importance of each node
 . While this importance should be close to the illumination ...\n\n\nYusuk
 e Tokuyoshi, Sho Ikeda, Paritosh Kulkarni, and Takahiro Harada (Advanced M
 icro Devices, Inc.)\n---------------------\nDARTS: Diffusion Approximated 
 Residual Time Sampling for Time-of-flight Rendering in Homogeneous Scatter
 ing Media\n\nTime-of-flight (ToF) devices have greatly propelled the advan
 cement of various multi-modal perception applications. However, achieving 
 accurate rendering of time-resolved information remains a challenge, parti
 cularly in scenes involving complex geometries, diverse materials and part
 icipating media. ...\n\n\nQianyue He, Dongyu Du, Haitian Jiang, and Xin Ji
 n (Tsinghua Shenzhen International Graduate School)\n---------------------
 \nA Generalized Ray Formulation For Wave-Optical Light Transport\n\nRay op
 tics is the foundation of modern path tracing and sampling algorithms for 
 computer graphics; crucially, it allows high-performance implementations b
 ased on ray tracing. However, many applications of interest in computer gr
 aphics and computational optics demand a more precise understanding of l..
 .\n\n\nShlomi Steinberg (University of Waterloo); Ravi Ramamoorthi (NVIDIA
 , University of California San Diego); Benedikt Bitterli and Eugene d'Eon 
 (NVIDIA); Ling-Qi Yan (University of California Santa Barbara); and Matt P
 harr (NVIDIA)\n\nRegistration Category: Full Access, Full Access Supporter
 \n\nLanguage Format: English Language\n\nSession Chair: Seungyong Lee (POS
 TECH)
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