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DTSTAMP:20240214T070312Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231215T101500
DTEND;TZID=Australia/Melbourne:20231215T111500
UID:siggraphasia_SIGGRAPH Asia 2023_sess154@linklings.com
SUMMARY:See Through The Field
DESCRIPTION:Technical Papers\n\nMCNeRF: Monte Carlo Rendering and Denoisin
 g for Real-Time NeRFs\n\nThe volume rendering step used in Neural Radiance
  Fields (NeRFs) produces highly photorealistic results, but is inherently 
 slow because it evaluates an MLP at a large number of sample points per ra
 y. Previous work has addressed this by either proposing neural scene repre
 sentations that are faster to...\n\n\nKunal Gupta (UC San Diego); Milos Ha
 san, Zexiang Xu, Fujun Luan, Kalyan Sunkavalli, and Xin Sun (Adobe Inc.); 
 Manmohan Chandraker (UC San Diego); and Sai Bi (Adobe Inc.)\n-------------
 --------\nAdaptive Shells for Efficient Neural Radiance Field Rendering\n\
 nNeural radiance fields achieve unprecedented quality for novel view synth
 esis, but their volumetric formulation remains expensive, requiring a huge
  number of samples to render high-resolution images. Volumetric encodings 
 are essential to represent fuzzy geometry such as foliage and hair, and th
 ey ar...\n\n\nZian Wang and Tianchang Shen (NVIDIA, University of Toronto)
 ; Merlin Nimier-David and Nicholas Sharp (NVIDIA); Jun Gao (NVIDIA, Univer
 sity of Toronto); Alexander Keller (NVIDIA); Sanja Fidler (NVIDIA, Univers
 ity of Toronto); and Thomas Müller and Zan Gojcic (NVIDIA)\n--------------
 -------\nScaNeRF: Scalable Bundle-Adjusting Neural Radiance Fields for Lar
 ge-Scale Scene Rendering\n\nHigh-quality large-scale scene rendering requi
 res a scalable representation and accurate camera poses. This research com
 bines tile-based hybrid neural fields with parallel distributive optimizat
 ion to improve bundle-adjusting neural radiance fields. The proposed metho
 d scales with a divide-and-conqu...\n\n\nXiuchao Wu (State Key Laboratory 
 of CAD & CG, Zhejiang University); Jiamin Xu (Hangzhou Dianzi Univeristy);
  Xin Zhang (State Key Laboratory of CAD&CG, Zhejiang Univerisity); Hujun B
 ao (State Key Laboratory of CAD & CG, Zhejiang University); Qixing Huang (
 University of Texas at Austin); Yujun Shen (Ant Group); James Tompkin (Bro
 wn University); and Weiwei Xu (State Key Laboratory of CAD&CG, Zhejiang Un
 iverisity)\n---------------------\nActRay: Online Active Ray Sampling for 
 Radiance Fields\n\nThanks to the high-quality reconstruction and photoreal
 istic rendering, the Neural Radiance Field (NeRF) has garnered extensive a
 ttention and has been continuously improved. Despite its high visual quali
 ty, the prohibitive training time limits its practical application. Althou
 gh significant accelera...\n\n\nJiangkai Wu, Liming Liu, Yunpeng Tan, Quan
 lu Jia, Haodan Zhang, and Xinggong Zhang (Peking University)\n------------
 ---------\nRT-Octree: Accelerate PlenOctree Rendering with Batched Regular
  Tracking and Neural Denoising for Real-time Neural Radiance Fields\n\nNeu
 ral Radiance Fields (NeRF) has demonstrated its ability to generate high-q
 uality synthesized views. Nonetheless, due to its slow inference speed, th
 ere is a need to explore faster inference methods. In this paper, we propo
 se RT-Octree, which uses batched regular tracking based on PlenOctree with
  ...\n\n\nZixi Shu, Ran Yi, Yuqi Meng, Yutong Wu, and Lizhuang Ma (Shangha
 i Jiao Tong University)\n\nRegistration Category: Full Access\n\nSession C
 hair: Yuchi Huo (Zhejiang University, Korea Advanced Institute of Science 
 and Technology)
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