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DTSTAMP:20240214T070312Z
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:Technical Papers\n\nCompact Neural Graphic Primitives with Lea
 rned Hash Probing\n\nNeural graphics primitives are faster and achieve hig
 her quality when their neural networks are augmented by spatial data struc
 tures that hold trainable features arranged in a grid. However, existing f
 eature grids either come with a large memory footprint (dense or factorize
 d grids, trees, and hash ...\n\n\nTowaki Takikawa (NVIDIA, University of T
 oronto); Thomas Müller, Merlin Nimier-David, and Alex Evans (NVIDIA); Sanj
 a Fidler (NVIDIA, University of Toronto); Alec Jacobson (University of Tor
 onto, Adobe Research); and Alexander Keller (NVIDIA)\n--------------------
 -\nNeural-Singular-Hessian: Implicit Neural Representation of Unoriented P
 oint Clouds by Enforcing Singular Hessian\n\nNeural implicit representatio
 n is a promising approach for reconstructing surfaces from point clouds. E
 xisting methods combine various regularization terms to enforce the learne
 d 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, Yu
 nxiao Zhang, and Rui Xu (Shandong University); Fan Zhang (Shandong Technol
 ogy and Business University); Peng-Shuai Wang (Peking University); Shuangm
 in Chen (Qingdao University of Science and Technology); Shiqing Xin (Shand
 ong University); Wenping Wang (Texas A&M University); and Changhe Tu (Shan
 dong University)\n---------------------\nNeural Gradient Learning and Opti
 mization for Oriented Point Normal Estimation\n\nWe propose Neural Gradien
 t Learning (NGL), a deep learning approach to learn gradient vectors with 
 consistent orientation from 3D point clouds for normal estimation. It has 
 excellent gradient approximation properties for the underlying geometry of
  the data. We utilize a simple neural network to para...\n\n\nQing Li (Tsi
 nghua University), Huifang Feng (Xiamen University), Kanle Shi (Kuaishou T
 echnology), Yi Fang (New York University), Yu-Shen Liu (Tsinghua Universit
 y), and Zhizhong Han (Wayne State University)\n---------------------\nAnti
 -Aliased Neural Implicit Surfaces with Encoding Level of Detail\n\nWe pres
 ent LoD-NeuS, an efficient neural representation for high-frequency geomet
 ry detail recovery and anti-aliased novel view rendering. Drawing inspirat
 ion from voxel-based representations with the level of detail (LoD), we in
 troduce a multi-scale tri-plane-based scene representation 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---------------------\n
 Constructive Solid Geometry on Neural Signed Distance Fields\n\nSigned Dis
 tance Fields (SDFs) parameterized by neural networks have recently gained 
 popularity as a fundamental geometric representation. However, editing the
  shape encoded by a neural SDF remains an open challenge.  A tempting appr
 oach is to leverage common geometric operators (e.g., boolean operat...\n\
 n\nZoë Marschner (Massachusetts Institute of Technology, Carnegie Mellon U
 niversity); Silvia Sellán (University of Toronto); Hsueh-Ti Derek Liu (Ro
 blox Research); and Alec Jacobson (University of Toronto)\n\nRegistration 
 Category: Full Access\n\nSession Chair: Fei Hou (Institute of Software, Ch
 inese Academy of Sciences; University of Chinese Academy of Sciences)
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