title: DC3DO: Diffusion Classifier for 3D Objects
summary: Inspired by Geoffrey Hinton emphasis on generative modeling, To recognize
  shapes, first learn to generate them, we explore the use of 3D diffusion models
  for object classification. Leveraging the density estimates from these models,
  our approach, the Diffusion Classifier for 3D Objects (DC3DO), enables
  zero-shot classification of 3D shapes without additional training. On average,
  our method achieves a 12.5 percent improvement compared to its multiview
  counterparts, demonstrating superior multimodal reasoning over discriminative
  approaches. DC3DO employs a class-conditional diffusion model trained on
  ShapeNet, and we run inferences on point clouds of chairs and cars. This work
  highlights the potential of generative models in 3D object classification.
paper_authors._id: 66bc26783a141b6952bbbeef
paper_authors.name: Nursena Koprucu
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paper_authors.fullname: Nursena Koprucu
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paper_authors.name: Meher Shashwat Nigam
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paper_authors.fullname: Meher Shashwat Nigam
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paper_authors.name: Shicheng Xu
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paper_authors.name: Biruk Abere
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paper_authors.fullname: Biruk Abere
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paper_authors.name: Gabriele Dominici
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paper_authors.fullname: Gabriele Dominici
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paper_authors.user: gabriele-dominici
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paper_authors.name: Andrew Rodriguez
paper_authors._id: 66bc26783a141b6952bbbef5
paper_authors.name: Sharvaree Vadgam
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paper_authors.fullname: Sharvaree Vadgama
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paper_authors.user: Sharvareev
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paper_authors.name: Berfin Inal
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paper_authors._id: 6415b03558a690df103b937a
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paper_authors.fullname: Berfin Inal
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paper_authors.user: sbi
paper_authors._id: 66bc26783a141b6952bbbef7
paper_authors.name: Alberto Tono
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T11:05:35.041Z
paper_authors._id: 6296401d7d586f66c3918208
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paper_authors.fullname: Alberto Tono
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paper_authors.user: tono
paper_title: DC3DO: Diffusion Classifier for 3D Objects
paper_summary: Inspired by Geoffrey Hinton emphasis on generative modeling, To recognize
  shapes, first learn to generate them, we explore the use of 3D diffusion models
  for object classification. Leveraging the density estimates from these models,
  our approach, the Diffusion Classifier for 3D Objects (DC3DO), enables
  zero-shot classification of 3D shapes without additional training. On average,
  our method achieves a 12.5 percent improvement compared to its multiview
  counterparts, demonstrating superior multimodal reasoning over discriminative
  approaches. DC3DO employs a class-conditional diffusion model trained on
  ShapeNet, and we run inferences on point clouds of chairs and cars. This work
  highlights the potential of generative models in 3D object classification.
paper_ai_summary: Diffusion Classifier for 3D Objects (DC3DO) uses class-conditional diffusion models to achieve zero-shot classification of 3D shapes with superior multimodal reasoning.
paper_ai_keywords: diffusion models generative modeling density estimates zero-shot classification multiview counterparts class-conditional diffusion model ShapeNet point clouds multimodal reasoning