DC3DO: Diffusion Classifier for 3D Objects
Authors: name: Nursena Koprucu, name: Meher Shashwat Nigam, name: Shicheng Xu, name: Biruk Abere, name: Gabriele Dominici, name: Andrew Rodriguez, name: Sharvaree Vadgam, name: Berfin Inal, name: Alberto Tono

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.