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DTSTAMP:20260114T163709Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231215T154000
DTEND;TZID=Australia/Melbourne:20231215T163500
UID:siggraphasia_SIGGRAPH Asia 2023_sess159@linklings.com
SUMMARY:Neural Shape Representation
DESCRIPTION:Neural-Singular-Hessian: Implicit Neural Representation of Uno
 riented Point Clouds by Enforcing Singular Hessian\n\nNeural implicit repr
 esentation is a promising approach for reconstructing surfaces from point 
 clouds. Existing methods combine various regularization terms to enforce t
 he learned neural function to possess the properties of a SDF, such as the
  Eikonal term and Laplacian energy term. However, when the...\n\n\nZixiong
  Wang, Yunxiao Zhang, and Rui Xu (Shandong University); Fan Zhang (Shandon
 g Technology and Business University); Peng-Shuai Wang (Peking University)
 ; Shuangmin Chen (Qingdao University of Science and Technology); Shiqing X
 in (Shandong University); Wenping Wang (Texas A&M University); and Changhe
  Tu (Shandong University)\n---------------------\nNeural Gradient Learning
  and Optimization for Oriented Point Normal Estimation\n\nWe propose Neura
 l Gradient Learning (NGL), a deep learning approach to learn gradient vect
 ors with consistent orientation from 3D point clouds for normal estimation
 . It has excellent gradient approximation properties for the underlying ge
 ometry of the data. We utilize a simple neural network to para...\n\n\nQin
 g Li (Tsinghua University), Huifang Feng (Xiamen University), Kanle Shi (K
 uaishou Technology), Yi Fang (New York University), Yu-Shen Liu (Tsinghua 
 University), and Zhizhong Han (Wayne State University)\n------------------
 ---\nConstructive Solid Geometry on Neural Signed Distance Fields\n\nSigne
 d Distance Fields (SDFs) parameterized by neural networks have recently ga
 ined popularity as a fundamental geometric representation. However, editin
 g the shape encoded by a neural SDF remains an open challenge.  A tempting
  approach is to leverage common geometric operators (e.g., boolean operat.
 ..\n\n\nZoë Marschner (Massachusetts Institute of Technology, Carnegie Mel
 lon University); Silvia Sellán (University of Toronto); Hsueh-Ti Derek Li
 u (Roblox Research); and Alec Jacobson (University of Toronto)\n----------
 -----------\nAnti-Aliased Neural Implicit Surfaces with Encoding Level of 
 Detail\n\nWe present LoD-NeuS, an efficient neural representation for high
 -frequency geometry detail recovery and anti-aliased novel view rendering.
  Drawing inspiration from voxel-based representations with the level of de
 tail (LoD), we introduce a multi-scale tri-plane-based scene representatio
 n that is capa...\n\n\nYiyu Zhuang (Nanjing University); Qi Zhang and Ying
  Feng (Tencent); Hao Zhu and Yao Yao (Nanjing University); Xiaoyu Li, Yan-
 Pei Cao, and Ying Shan (Tencent); and Xun Cao (Nanjing University)\n------
 ---------------\nCompact Neural Graphic Primitives with Learned Hash Probi
 ng\n\nNeural graphics primitives are faster and achieve higher quality whe
 n their neural networks are augmented by spatial data structures that hold
  trainable features arranged in a grid. However, existing feature grids ei
 ther come with a large memory footprint (dense or factorized grids, trees,
  and hash ...\n\n\nTowaki Takikawa (NVIDIA, University of Toronto); Thomas
  Müller, Merlin Nimier-David, and Alex Evans (NVIDIA); Sanja Fidler (NVIDI
 A, University of Toronto); Alec Jacobson (University of Toronto, Adobe Res
 earch); and Alexander Keller (NVIDIA)\n\nRegistration Category: Full Acces
 s\n\nSession Chair: Fei Hou (Institute of Software, Chinese Academy of Sci
 ences; University of Chinese Academy of Sciences)
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