Presentation / Installation


3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
SessionDiffusion for Geometry
DescriptionThis paper presents 3DShape2VecSet, a novel shape representation that encodes a 3D shape using neural fields as a set of vectors. The representation is designed for generative diffusion models and outperforms other methods in various 3D shape encoding and generative modeling tasks, including point-cloud completion and category/text/image-conditioned generation.
Authors
Biao Zhang
King Abdullah University of Science and Technology (KAUST)
Jiapeng Tang
Technical University of Munich
Matthias Nießner
Technical University of Munich
Peter Wonka
King Abdullah University of Science and Technology (KAUST)
Event Type
Technical Paper
TimeWednesday, 9 August 20239:11am - 9:22am PDT
LocationRoom 502 AB
ACM Digital Library Technical Papers pdfs
Session Time & Location
Sunday, 6 August 20236pm - 8:30pm PDTWest Hall B
Wednesday, 9 August 20239am - 10:30am PDTRoom 502 AB
Interest Areas
Research & Education
Recordings
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Recorded
Keywords
Artificial Intelligence/Machine Learning
Modeling
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