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BEGIN:VEVENT
DTSTAMP:20250522T212946Z
LOCATION:Mile High 2A
DTSTART;TZID=America/Denver:20240801T090000
DTEND;TZID=America/Denver:20240801T091000
UID:siggraph_SIGGRAPH 2024_sess134_papers_1072@linklings.com
SUMMARY:fVDB : A Deep-learning Framework for Sparse, Large Scale, and High
  Performance Spatial Intelligence
DESCRIPTION:Francis Williams, Jiahui Huang, Jonathan Swartz, Gergely Klar,
  Vijay Thakkar, Matthew Cong, and Xuanchi Ren (NVIDIA Research); Ruilong L
 i (NVIDIA Research, University of California Berkeley); Clement Fuji Tsang
  and Sanja Fidler (NVIDIA Research); Eftychios Sifakis (University of Wisc
 onsin-Madison, NVIDIA Research); and Ken Museth (NVIDIA Research)\n\nWe in
 troduce fVDB, a GPU-optimized framework for deep learning on large-scale 3
 D data that efficiently accommodates spatial sparsity, based on a novel VD
 B index grid structure. Our framework is fully integrated with PyTorch and
  includes a comprehensive collection of operators for tasks such as convol
 ution, pooling, attention, and raytracing.\n\nInterest Area: Research & Ed
 ucation\n\nRecording: Livestreamed, Recorded\n\nKeyword: Geometry, Modelin
 g\n\nRegistration Category: Full Conference, Full Conference Supporter, Vi
 rtual Access, Exhibitor Full Conference, Thursday\n\nSession Chair: Tizian
  Zeltner (NVIDIA)\n\n
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