Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Koprucu, Nursena, Nigam, Meher Shashwat, Xu, Shicheng, Abere, Biruk, Dominici, Gabriele, Rodriguez, Andrew, Vadgama, Sharvaree, Inal, Berfin, Tono, Alberto
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4402427071