DC3DO: Diffusion Classifier for 3D Objects
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.
66bc26783a141b6952bbbeef
Nursena Koprucu
admin_assigned
2024-08-14T11:03:54.465Z
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Nursena Koprucu
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Nursena
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Meher Shashwat Nigam
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2024-08-14T11:04:00.188Z
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Meher Shashwat Nigam
user
MeherShashwat
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Shicheng Xu
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Biruk Abere
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2024-08-14T11:04:15.358Z
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Biruk Abere
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Biruk123
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Gabriele Dominici
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2024-08-14T11:04:21.218Z
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Gabriele Dominici
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gdominici
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Andrew Rodriguez
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Sharvaree Vadgam
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2024-08-14T11:05:23.828Z
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Sharvaree Vadgama
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Sharvareev
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Berfin Inal
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2024-08-14T11:05:29.254Z
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Berfin Inal
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sbi
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Alberto Tono
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2024-08-14T11:05:35.041Z
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Alberto Tono
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tono
DC3DO: Diffusion Classifier for 3D Objects
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.
Diffusion Classifier for 3D Objects (DC3DO) uses class-conditional diffusion models to achieve zero-shot classification of 3D shapes with superior multimodal reasoning.
diffusion models generative modeling density estimates zero-shot classification multiview counterparts class-conditional diffusion model ShapeNet point clouds multimodal reasoning