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DTSTAMP:20260408T204153Z
LOCATION:West Building\, Rooms 211-214
DTSTART;TZID=America/Los_Angeles:20250812T140000
DTEND;TZID=America/Los_Angeles:20250812T153000
UID:siggraph_SIGGRAPH 2025_sess103@linklings.com
SUMMARY:Reconstruction & Neural Fields
DESCRIPTION:Variational Surface Reconstruction Using Natural Neighbors\n\n
 We introduced a new surface reconstruction method from points without norm
 als. The method robustly handles undersampled regions and scales to large 
 input sizes.\n\n\nJianjun Xia and Tao Ju (Washington University in St. Lou
 is)\n---------------------\nStochastic Preconditioning for Neural Field Op
 timization\n\nStochastic preconditioning adds spatial noise to query locat
 ions during neural field optimization; it can be formalized as a stochasti
 c estimate for a blur operator. This simple technique eases optimization a
 nd significantly improves quality for neural fields optimization, matching
  or outperforming ...\n\n\nSelena Ling (NVIDIA, University of Toronto); Me
 rlin Nimier-David (NVIDIA); Alec Jacobson (University of Toronto); and Nic
 holas Sharp (NVIDIA)\n---------------------\nIMLS-Splatting: Efficient Mes
 h Reconstruction from Multi-view Images via Point Representation\n\nWe pro
 pose IMLS-Splatting, an end-to-end multi-view mesh optimization method tha
 t leverages point clouds for surface representation. By introducing a spla
 tting-based differentiable IMLS algorithm, our approach efficiently conver
 ts point clouds into SDF and texture field, enabling multi-view mesh opt..
 .\n\n\nKaizhi Yang (University of Science and Technology of China); Liu Da
 i and Isabella Liu (University of California San Diego); Xiaoshuai Zhang (
 Hillbot Inc.); Xiaoyan Sun and Xuejin Chen (University of Science and Tech
 nology of China); Zexiang Xu (Hillbot Inc.); and Hao Su (University of Cal
 ifornia San Diego, Hillbot Inc.)\n---------------------\nSpline Deformatio
 n Field\n\nWe combine splines,  a classical tool from applied mathematics,
  with implicit Coordinate Neural Networks to model deformation fields, ach
 ieving strong performance across multiple datasets. The explicit regulariz
 ation from spline interpolation enhances spatial coherency in challenging 
 scenarios. We f...\n\n\nMingyang Song (Disney Research Studios, ETH Zürich
 ); Yang Zhang (Disney Research Studios); Marko Mihajlovic and Siyu Tang (E
 TH Zürich); Markus Gross (ETH Zürich, Disney Research Studios); and Tunc O
 zan Aydin (Disney Research Studios)\n---------------------\nReconstruction
  & Neural Fields - Interactive Discussion\n\nAfter the summary presentatio
 ns, attendees will participate in an interactive discussion. Outside the r
 oom will be a series of poster boards for authors to gather around with th
 e audience. Authors are invited to bring any material related to their pap
 er that could instigate further conversation such...\n\n------------------
 ---\nDiffusing Winding Gradients (DWG): A Parallel and Scalable Method for
  3D Reconstruction from Unoriented Point Clouds\n\nDiffusing Winding Gradi
 ents (DWG) efficiently reconstructs watertight 3D surfaces from unoriented
  point clouds. Unlike conventional methods, DWG avoids solving linear syst
 ems or optimizing objective functions, enabling simple implementation and 
 parallel execution. Our CUDA implementation on an NVIDI...\n\n\nWeizhou Li
 u (Beijing Normal University); Jiaze Li (Nanyang Technological University)
 ; Xuhui Chen and Fei Hou (Institute of Software, Chinese Academy of Scienc
 es; University of Chinese Academy of Sciences); Shiqing Xin (Shandong Univ
 ersity); Xingce Wang and Zhongke Wu (Beijing Normal University); Chen Qian
  (SenseTime Group); Ying He (Nanyang Technological University  College of 
 Computing and Data Science); and Ying He\n---------------------\nCAST: Com
 ponent-Aligned 3D Scene Reconstruction from an RGB Image\n\nWe introduce C
 AST, an innovative method for reconstructing high-quality 3D scenes from a
  single RGB  image. Supporting open-vocabulary reconstruction, CAST excels
  in managing occlusions, aligning objects accurately, and ensuring physica
 l consistency with the input, unlocking new possibilities in vir...\n\n\nK
 aixin Yao, Longwen Zhang, Xinhao Yan, Yan Zeng, and Qixuan Zhang (Shanghai
 Tech University, Deemos); Jiayuan Gu (ShanghaiTech University); Wei Yang (
 Huazhong University of Science and Technology); and Lan Xu and Jingyi Yu (
 ShanghaiTech University)\n\nInterest Area: Research & Education\n\nRecordi
 ng: Livestreamed, Not Livestreamed, Recorded, Not Recorded\n\nRegistration
  Category: Full Conference, Virtual Access, Tuesday\n\nSession Chair: Zhao
  Dong (Meta Reality Labs Research)
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