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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
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TZOFFSETTO:+0900
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DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035346Z
LOCATION:Room 324\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T153000
DTEND;TZID=Asia/Seoul:20221206T170000
UID:siggraphasia_SIGGRAPH Asia 2022_sess155@linklings.com
SUMMARY:Differentiable Rendering
DESCRIPTION:Technical Communications, Technical Papers\n\nThe presentation
s will be followed by a 30-min Interactive Discussion Session at Room 325-
CD.\n\nThe Technical Papers program is the premier international forum for
disseminating new scholarly work in computer graphics and interactive tec
hniques. Technical Papers are published as a special issue of ACM Transact
ions on Graphics. In addition to papers selected by the SIGGRAPH Asia 2022
Technical Papers Jury, the conference presents papers that have been publ
ished in ACM Transactions on Graphics during the past year. Accepted paper
s adhere to the highest scientific standards.\n\nThe Technical Communicati
ons program is a premier forum for presenting the latest developments and
research still in progress. Leading international experts in academia and
industry present work that showcase actual implementations of research ide
as, works at the crossroads of computer graphics with computer vision, mac
hine learning, HCI, VR, CAD, visualization, and many others\n\nEfficient D
ifferentiation of Pixel Reconstruction Filters for Path-Space Differentiab
le Rendering\n\nYu, Zhang, Dong, Nowrouzezahrai, Zhao\n\nPixel reconstruct
ion filters play an important role in physics-based rendering and have bee
n thoroughly studied. In physics-based differentiable rendering, however,
the proper treatment of pixel reconstruction filters has remained largely
under-explored. We present a new technique to efficiently dif...\n\n------
---------------\nDepth of Field Aware Differentiable Rendering\n\nPidhorsk
yi, Bagautdinov, Ma, Saragih, Schwartz...\n\nCameras with a finite apertur
e diameter exhibit defocus for scene elements that are not at the focus di
stance and have only a limited depth of field within which objects appear
acceptably sharp. In this work, we address the problem of applying inverse
rendering techniques to input data that exhibits...\n\n------------------
---\nLearning-based Inverse Rendering of Complex Indoor Scenes with Differ
entiable Monte Carlo Raytracing\n\nZhu, Luan, Huo, Lin, Zhong...\n\nIndoor
scenes typically exhibit complex, spatially-varying appearance from globa
l illumination, making the inverse rendering a challenging ill-posed probl
em. This work presents an end-to-end, learning-based inverse rendering fra
mework incorporating differentiable Monte Carlo raytracing with importan..
.\n\n---------------------\nOptical Parameter Estimation for Hair and Fur
using Differentiable Rendering\n\nShibaike, Iwasaki\n\nWe propose a parame
ter estimation method of Bidirectional Curve Scattering Distribution Funct
ion for hair and fur using differentiable rendering.\n\n------------------
---\nDifferentiable Point-Based Radiance Fields for Efficient View Synthes
is\n\nZhang, Baek, Rusinkiewicz, Heide\n\nWe propose a differentiable rend
ering algorithm for efficient novel view synthesis. By departing from volu
me-based representations in favor of a learned point representation, we im
prove on existing methods more than an order of magnitude in memory and ru
ntime, both in training and inference. The met...\n\n---------------------
\nDifferentiable Rendering of Neural SDFs through Reparameterization\n\nBa
ngaru, Gharbi, Luan, Li, Sunkavalli...\n\nWe present a method to automatic
ally compute correct gradients with respect to geometric scene parameters
in neural SDF renderers. Recent physically-based differentiable rendering
techniques for meshes have used edge-sampling to handle discontinuities, p
articularly at object silhouettes, but SDFs do...\n\n---------------------
\nDifferentiable rendering using RGBXY derivatives and optimal transport\n
\nXing, Luan, Yan, Hu, Qian...\n\nWe present a novel differentiable render
ing framework that is reformulated from the Lagrangian view. Inspired by f
luid simulation, traditional differentiable rendering approach can be view
ed as the Eulerian method due to the fact that image derivatives are compu
ted locally on a fixed grid of screen-...\n\n\nRegistration Category: FULL
ACCESS, ON-DEMAND ACCESS\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON, ON-DE
MAND
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