We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant latent representation. This enables efficient training with low-resolution point clouds, while allowing high-resolution point clouds to be generated during inference. More importantly, we show that scaling the test-time resolution beyond the training resolution improves the fidelity of generated point clouds and surfaces. We analyze this phenomenon and draw a link to classifier-free guidance commonly used in diffusion models, demonstrating that both allow trading off fidelity and variability during inference. Experiments on CO3D show that PointInfinity can efficiently generate high-resolution point clouds (up to 131k points, 31 times more than Point-E) with state-of-the-art quality.
@article{arxiv.2404.03566,
title = {PointInfinity: Resolution-Invariant Point Diffusion Models},
author = {Zixuan Huang and Justin Johnson and Shoubhik Debnath and James M. Rehg and Chao-Yuan Wu},
journal= {arXiv preprint arXiv:2404.03566},
year = {2024}
}
Comments
Accepted to CVPR 2024, project website at https://zixuanh.com/projects/pointinfinity