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.

翻译：受Geoffrey Hinton“为识别形状，先学会生成它们”这一对生成建模的强调所启发，我们探索了使用三维扩散模型进行物体分类。我们的方法——面向三维物体的扩散分类器（DC3DO）——利用这些模型的密度估计，实现了无需额外训练的三维形状零样本分类。平均而言，我们的方法相比其多视图对应方法取得了12.5%的性能提升，展现了优于判别式方法的多模态推理能力。DC3DO采用在ShapeNet上训练的类别条件扩散模型，并在椅子与汽车的点云数据上进行推理。这项工作凸显了生成模型在三维物体分类中的潜力。