  We introduce 3DShape2VecSet, a novel shape representation for neural fields
designed for generative diffusion models. Our shape representation can encode
3D shapes given as surface models or point clouds, and represents them as
neural fields. The concept of neural fields has previously been combined with a
global latent vector, a regular grid of latent vectors, or an irregular grid of
latent vectors. Our new representation encodes neural fields on top of a set of
vectors. We draw from multiple concepts, such as the radial basis function
representation and the cross attention and self-attention function, to design a
learnable representation that is especially suitable for processing with
transformers. Our results show improved performance in 3D shape encoding and 3D
shape generative modeling tasks. We demonstrate a wide variety of generative
applications: unconditioned generation, category-conditioned generation,
text-conditioned generation, point-cloud completion, and image-conditioned
generation.
