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BEGIN:VEVENT
DTSTAMP:20250522T212942Z
LOCATION:Mile High 1
DTSTART;TZID=America/Denver:20240801T145000
DTEND;TZID=America/Denver:20240801T150000
UID:siggraph_SIGGRAPH 2024_sess144_papers_858@linklings.com
SUMMARY:TensoSDF: Roughness-aware Tensorial Representation for Robust Geom
 etry and Material Reconstruction
DESCRIPTION:Jia Li (Shandong University), Beibei Wang (Nanjing University)
 , Lu Wang (Shandong University), and Lei Zhang (The Hong Kong Polytechnic 
 University)\n\nWe propose a novel framework for robust geometry and materi
 al reconstruction. The framework's core is the roughness-aware incorporati
 on of the radiance and reflectance fields to reconstruct arbitrary reflect
 ive objects. The proposed TensoSDF representation enhances the geometry de
 tails while accelerating the training, and the explicit-implicit fusion st
 rategy enables accurate material estimation.\n\nInterest Area: Research & 
 Education\n\nRecording: Livestreamed, Recorded\n\nRegistration Category: F
 ull Conference, Full Conference Supporter, Virtual Access, Exhibitor Full 
 Conference, Thursday\n\nSession Chair: Nathan Carr (Adobe)\n\n
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