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BEGIN:VEVENT
DTSTAMP:20250522T212947Z
LOCATION:Mile High 2A
DTSTART;TZID=America/Denver:20240729T140000
DTEND;TZID=America/Denver:20240729T153000
UID:siggraph_SIGGRAPH 2024_sess110@linklings.com
SUMMARY:Geometry: Reconstruction
DESCRIPTION:NeurCADRecon: Neural Representation for Reconstructing CAD Sur
 faces by Enforcing Zero Gaussian Curvature\n\nIn this paper, we present a 
 self-supervised neural representation aimed at reconstructing CAD models f
 rom low-quality, unoriented point clouds. Our approach stands out from tra
 ditional reconstruction methods by applying a zero Gaussian curvature cons
 traint, which emphasizes the characteristic of bei...\n\n\nQiujie Dong and
  Rui Xu (Shandong University); Pengfei Wang (University of Hong Kong); Shu
 angmin Chen (Qingdao University of Science and Technology); Shiqing Xin (S
 handong University); Xiaohong Jia (AMSS, Chinese Academy of Sciences); Wen
 ping Wang (Texas A&M University); and Changhe Tu (Shandong University)\n--
 -------------------\nHigh-quality Surface Reconstruction Using Gaussian Su
 rfels\n\nWe introduce Gaussian surfels, a novel point-based representation
  that flattens 3D Gaussian ellipsoids into 2D ellipses. This representatio
 n combines the advantages of the flexible optimization procedure in 3D Gau
 ssian points and the surface alignment property of surfels, resulting in s
 uperior perfo...\n\n\nPinxuan Dai (State Key Laboratory of CAD & CG, Zheji
 ang University); Jiamin Xu (Hangzhou Dianzi University); Wenxiang Xie and 
 Xinguo Liu (State Key Laboratory of CAD & CG, Zhejiang University); Huamin
  Wang (Style3D Research); and Weiwei Xu (State Key Laboratory of CAD & CG,
  Zhejiang University)\n---------------------\nGeometry: Reconstruction - I
 nteractive Discussion\n\nAfter the summary presentations, attendees will p
 articipate in an interactive discussion. Distributed around the room will 
 be a series of poster boards for authors to gather around with the audienc
 e. Authors are invited to bring any material related to their paper that c
 ould instigate further conver...\n\n---------------------\nPart123: Part-a
 ware 3D Reconstruction From a Single-view Image\n\nThis paper proposes Par
 t123, a novel framework for part-aware 3D reconstruction from a single-vie
 w image. Based on the multiview images generated by multiview diffusion, w
 e apply contrastive learning to incorporate their 2D image segmentations i
 nto the 3D reconstruction process, thus enabling the ge...\n\n\nAnran Liu,
  Cheng Lin, Yuan Liu, Xiaoxiao Long, and Zhiyang Dou (University of Hong K
 ong); Hao-Xiang Guo (Tsinghua University); Ping Luo (University of Hong Ko
 ng); and Wenping Wang (Texas A&M University)\n---------------------\nNeura
 lTO: Neural Reconstruction and View Synthesis of Translucent Objects\n\nWe
  introduce a novel, two-stages framework, which is geared toward high-fide
 lity surface reconstruction and the novel-view synthesis of translucent ob
 jects. In our framework, we propose a theoretical model for the neural rad
 iance field of translucent objects, which parametrizes the density field u
 si...\n\n\nYuxiang Cai and Jiaxiong Qiu (Nankai University, TMCC); Zhong L
 i (OPPO US Research Center); and Bo Ren (Nankai University, TMCC)\n-------
 --------------\nMVD^2: Efficient Multiview 3D Reconstruction for Multiview
  Diffusion\n\nMultiview diffusion (MVD) has emerged as a prominent 3D gene
 ration technique, but faces challenges with inconsistency and view sparsen
 ess, impacting the quality of multiview 3D reconstruction. Our learning-ba
 sed MVD^2 method tackles these challenges, ensuring efficient and robust 3
 D reconstruction w...\n\n\nXin-Yang Zheng (Tsinghua University, Microsoft 
 Research Asia); Hao Pan, Yu-Xiao Guo, and Xin Tong (Microsoft Research Asi
 a); and Yang Liu (Microsoft)\n---------------------\nReach for the Arcs: R
 econstructing Surfaces From SDFs via Tangent Points\n\nWe introduce an alg
 orithm to reconstruct a mesh from discrete signed distance function (SDF) 
 samples. We use the information contained in the SDF to construct an orien
 ted point cloud that is then converted into a triangle mesh. Our method ha
 s no restrictions on topology.\n\n\nSilvia Sellán (University of Toronto),
  Yingying Ren (EPFL), Christopher Batty (University of Waterloo), and Oded
  Stein (University of Southern California)\n\nInterest Area: Research & Ed
 ucation\n\nKeyword: Geometry, Modeling\n\nRegistration Category: Full Conf
 erence, Full Conference Supporter, Virtual Access, Exhibitor Full Conferen
 ce, Monday\n\nSession Chair: Kari Pulli (Google)
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