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DTSTART:18871231T000000
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DTSTAMP:20260817T171532Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241203T130000
DTEND;TZID=Asia/Tokyo:20241203T141000
UID:siggraphasia_SIGGRAPH Asia 2024_sess104@linklings.com
SUMMARY:Going Big in Rendering
DESCRIPTION:Each Paper gives a 10 minute presentation.\n\nInfNeRF: Towards
  Infinite Scale NeRF Rendering with O(log n) Space Complexity\n\nThe conve
 ntional mesh-based Level of Detail (LoD) technique, exemplified by applica
 tions such as Google Earth and many game engines, exhibits the capability 
 to holistically represent a large scene even the Earth, and achieves rende
 ring with a space complexity of O(log n).\nThis constrained data requi...\
 n\n\nJiabin Liang and Lanqing Zhang (Sea AI Lab), Zhuoran Zhao (National U
 niversity of Singapore), and Xiangyu Xu (Xi'an Jiaotong University)\n-----
 ----------------\nTaming 3DGS: High-Quality Radiance Fields with Limited R
 esources\n\n3D Gaussian Splatting (3DGS) has transformed novel-view synthe
 sis with its fast, interpretable, and high-fidelity rendering. However, it
 s resource requirements limit its usability: Especially on weaker or const
 rained devices, training performance degrades quickly and often cannot com
 plete due to exc...\n\n\nSaswat Subhajyoti Mallick (Carnegie Mellon Univer
 sity); Rahul Goel (International Institute of Information Technology, Hyde
 rabad); Bernhard Kerbl (Carnegie Mellon University); Markus Steinberger (G
 raz University of Technology); and Francisco Vicente Carrasco and Fernando
  De La Torre (Carnegie Mellon University)\n---------------------\nRepresen
 ting Long Volumetric Video with Temporal Gaussian Hierarchy\n\nThis paper 
 aims to address the challenge of reconstructing long volumetric videos fro
 m multi-view RGB videos.\nRecent dynamic view synthesis methods leverage p
 owerful 4D representations, like feature grids or point cloud sequences, t
 o achieve high-quality rendering results. However, they are typicall...\n\
 n\nZhen Xu (State Key Laboratory of CAD&CG, Zhejiang University; Zhejiang 
 University); Yinghao Xu (Stanford University); Zhiyuan Yu (Department of M
 athematics, Hong Kong University of Science and Technology); Sida Peng and
  Jiaming Sun (Zhejiang University); and Hujun Bao and Xiaowei Zhou (State 
 Key Laboratory of CAD&CG, Zhejiang University)\n---------------------\nLet
 sGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian
  Primitives\n\nLarge garages are ubiquitous yet intricate scenes that pres
 ent unique challenges due to their monotonous colors, repetitive patterns,
  reflective surfaces, and transparent vehicle glass. Conventional Structur
 e from Motion (SfM) methods for camera pose estimation and 3D reconstructi
 on often fail in th...\n\n\nJiadi Cui (ShanghaiTech University, Stereye In
 c.); Junming Cao (Shanghai Advanced Research Institute, Chinese Academy of
  Sciences; University of Chinese Academy of Sciences); Fuqiang Zhao (Shang
 haiTech University, NeuDim Inc.); Zhipeng He and Yifan Chen (ShanghaiTech 
 University); Yuhui Zhong (DGene Inc.); Lan Xu and Yujiao Shi (ShanghaiTech
  University); Yingliang Zhang (DGene Inc.); and Jingyi Yu (ShanghaiTech Un
 iversity)\n---------------------\nMVImgNet2.0: A Larger-scale Dataset of M
 ulti-view Images\n\nMVImgNet is a large-scale dataset that contains multi-
 view images of ~220k real-world objects in 238 classes. As a counterpart o
 f ImageNet, it introduces 3D visual signals via multi-view shooting, makin
 g a soft bridge between 2D and 3D vision. This paper constructs the MVImgN
 et2.0 dataset that expan...\n\n\nXiaoguang Han (SSE, The Chinese Universit
 y of Hong Kong, Shenzhen; FNii, The Chinese University of Hong Kong, Shenz
 hen); Yushuang Wu, Luyue Shi, Haolin Liu, Hongjie Liao, and Lingteng Qiu (
 FNii, The Chinese University of Hong Kong, Shenzhen; SSE, The Chinese Univ
 ersity of Hong Kong, Shenzhen); Weihao Yuan, Xiaodong Gu, and Zilong Dong 
 (Alibaba); and Shuguang Cui (SSE, The Chinese University of Hong Kong, She
 nzhen; FNii, The Chinese University of Hong Kong, Shenzhen)\n\nRegistratio
 n Category: Full Access, Full Access Supporter\n\nLanguage Format: English
  Language\n\nSession Chair: Bernhard Kerbl (Technical University of Vienna
 )
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