DC3DO：用于3D物体的扩散分类器

DC3DO: Diffusion Classifier for 3D Objects

August 13, 2024

作者: Nursena Koprucu, Meher Shashwat Nigam, Shicheng Xu, Biruk Abere, Gabriele Dominici, Andrew Rodriguez, Sharvaree Vadgam, Berfin Inal, Alberto Tono

cs.AI

摘要

受Geoffrey Hinton强调生成建模的启发，即要识别形状，首先要学会生成它们，我们探讨了使用3D扩散模型进行物体分类的方法。利用这些模型的密度估计，我们的方法，即用于3D物体的扩散分类器（DC3DO），实现了无需额外训练即可对3D形状进行零样本分类。平均而言，我们的方法相较于其多视图对应物取得了12.5%的改进，展示了优于判别方法的优越多模态推理能力。DC3DO采用在ShapeNet上训练的类条件扩散模型，并在椅子和汽车的点云上进行推断。这项工作突显了生成模型在3D物体分类中的潜力。

English

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.