Learning Gradient Fields for Shape Generation
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Diffusion models have shown potential in computer vision, natural language processing, temporal data modeling, multi-modal modeling, robust machine learning, computational chemistry, and medical image reconstruction.
“and have also shown potential in a variety of domains, ranging from computer vision [2,11,19,23,88,90,113,115,134,150,160,173,198,200,248,270,271,283,290], natural language processing [6,93,139,205,275], temporal data modeling [1,33,124,191,233,262], multi-modal modeling [7,186,196,199,288], robust machine learning [18,28,112,242,273], to interdisciplinary applications in fields such as computa”
From Diffusion Models: A Comprehensive Survey of Methods and Applications (2024) · cites this paper as [23]
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- Authors
- Cai, R., Yang, G., Averbuch-Elor, H., Hao, Z., Belongie, S., Snavely, N., & Hariharan, B.
- Affiliations
- Cornell University (US)
- Corresponding author
- Cai, Ruojin
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 364-381
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3108645709
- Cited by
- 225
- References
- 53
- Citations by year
- 2026: 17 · 2025: 36 · 2024: 66 · 2023: 41 · 2022: 29 · 2021: 33 · 2020: 3
- FWCI
- 23.57
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- point cloud generation, shape generation, gradient field, score-based generative models, stochastic gradient ascent, implicit surfaces, 3D shape modeling
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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