PointInfinity: Resolution-Invariant Point Diffusion Models - Huang, Zixuan; Johnson, Justin; Debnath, Shoubhik; Rehg, James M.; Wu, Chao-Yuan (Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online), 2024)
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× more than Point-E) with state-of-the-art quality.