PointInfinity: Resolution-Invariant Point Diffusion Models
<|reference_start|>PointInfinity: Resolution-Invariant Point Diffusion Models: 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.<|reference_end|>
@article{huang2024pointinfinity:,
    title={PointInfinity: Resolution-Invariant Point Diffusion Models},
    author={Zixuan Huang, Justin Johnson, Shoubhik Debnath, James M. Rehg,
  Chao-Yuan Wu},
    journal={arXiv preprint arXiv:2404.03566},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03566},
    primaryClass={cs.CV}
}