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BEGIN:VEVENT
DTSTAMP:20250110T023313Z
LOCATION:Hall B5 (1)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241206T104500
DTEND;TZID=Asia/Tokyo:20241206T115500
UID:siggraphasia_SIGGRAPH Asia 2024_sess142@linklings.com
SUMMARY:Modeling and Reconstruction
DESCRIPTION:Technical Papers\n\nEach Paper gives a 10 minute presentation.
 \n\nReconstruct translucent thin objects from photos\n\nThe joint reconstr
 uction of shape and appearance for translucent objects from real-world dat
 a poses a challenge in computer graphics, especially when dealing with com
 plex layered materials like leaves or paper. The traditional assumption of
  diffuse transmittance falls short, and more accurate Monte-...\n\n\nXi De
 ng (Cornell University); Lifan Wu (NVIDIA Research); Bruce Walter (Cornell
  University); Ravi Ramamoorthi (University of California San Diego, NVIDIA
  Research); Eugene d'Eon (NVIDIA Research); Steve Marschner (Cornell Unive
 rsity, NVIDIA Research); and Andrea Weidlich (NVIDIA Research)\n----------
 -----------\nDreamUDF: Generating Unsigned Distance Fields from A Single I
 mage\n\nRecent advances in diffusion models and neural implicit surfaces h
 ave shown promising progress in generating 3D models. However, existing ge
 nerative frameworks are limited to closed surfaces, failing to cope with a
  wide range of commonly seen shapes that have open boundaries. In this wor
 k, we presen...\n\n\nYu-Tao Liu and Xuan Gao (Institute of Computing Techn
 ology, Chinese Academy of Sciences; University of Chinese Academy of Scien
 ces); Weikai Chen (Tencent Games); Jie Yang (Institute of Computing Techno
 logy, Chinese Academy of Sciences; University of Chinese Academy of Scienc
 es); Xiaoxu Meng and Bo Yang (Tencent Games); and Lin Gao (Institute of Co
 mputing Technology, Chinese Academy of Sciences; University of Chinese Aca
 demy of Sciences)\n---------------------\nFaçAID: A Transformer Model for 
 Neuro-Symbolic Facade Reconstruction\n\nWe introduce a neuro-symbolic tran
 sformer-based model that converts flat, segmented facade structures into p
 rocedural definitions using a custom-designed split grammar. To facilitate
  this, we first develop a simple split grammar tailored for architectural 
 facades and then generate a dataset comprisi...\n\n\nAleksander Plocharski
  (Warsaw University of Technology, IDEAS NCBR); Jan Swidzinski (IDEAS NCBR
 ); Joanna Porter-Sobieraj (Warsaw University of Technology); and Przemysla
 w Musialski (New Jersey Institute of Technology, IDEAS NCBR)\n------------
 ---------\nStyle-NeRF2NeRF: 3D Style Transfer from Style-Aligned Multi-Vie
 w Images\n\nWe propose a simple yet effective pipeline for stylizing a 3D 
 scene, harnessing the power of 2D image diffusion models. Given a NeRF mod
 el reconstructed from a set of multi-view images, we perform 3D style tran
 sfer by refining the source NeRF model using stylized images generated by 
 a style-aligned ...\n\n\nHaruo Fujiwara (University of Tokyo) and Yusuke M
 ukuta and Tatsuya Harada (University of Tokyo, RIKEN AIP)\n---------------
 ------\nNU-NeRF: Neural Reconstruction of Nested Transparent Objects with 
 Uncontrolled Capture Environment\n\nThe reconstruction of transparent obje
 cts is a challenging problem due to the highly noncontinuous and rapidly c
 hanging surface color caused by refraction. Existing methods rely on speci
 al capture devices, dedicated backgrounds, or ground-truth object masks to
  provide more priors and reduce the ambi...\n\n\nJia-Mu Sun (Insititute of
  Computing Technology Chinese Academy of Sciences, KIRI Innovations); Tong
  Wu (Institute of Computing Technology, Chinese Academy of Sciences; Unive
 rsity of Chinese Academy of Sciences); Ling-Qi Yan (University of Californ
 ia Santa Barbara); and Lin Gao (Institute of Computing Technology, Chinese
  Academy of Sciences; University of Chinese Academy of Sciences)\n--------
 -------------\nLarge Scale Farm Scene Modeling from Remote Sensing Imagery
 \n\nIn this paper we propose a scalable framework for large-scale farm sce
 ne modeling that utilizes remote sensing data, specifically satellite imag
 es. Our approach begins by accurately extracting and categorizing the dist
 ributions of various scene elements from satellite images into four distin
 ct layer...\n\n\nZhiqi Xiao and Hao Jiang (Institute of Computing Technolo
 gy, Chinese Academy of Sciences; University of Chinese Academy of Sciences
 ); Zhigang Deng (University of Houston); and Ran Li, Wenwei Han, and Zhaoq
 i Wang (Institute of Computing Technology, Chinese Academy of Sciences; Un
 iversity of Chinese Academy of Sciences)\n\nRegistration Category: Full Ac
 cess, Full Access Supporter\n\nLanguage Format: English Language\n\nSessio
 n Chair: Maria Larsson (University of Tokyo)
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