论文标题
直流的快点云产生
Fast Point Cloud Generation with Straight Flows
论文作者
论文摘要
扩散模型已成为点云生成的强大工具。驱动令人印象深刻的性能以从噪声中产生高质量样本的关键组件是迭代的数千个步骤。虽然有益，但学习步骤的复杂性将其应用限制在许多3D现实世界中。为了解决这一限制，我们提出了点直流（PSF），该模型使用一个步骤表现出令人印象深刻的性能。我们的想法基于标准扩散模型的重新制定，该模型将弯曲的学习轨迹优化为直径。此外，我们制定了一种蒸馏策略，将直接路径缩短到没有性能损失的情况下，将应用程序应用于具有延迟约束的3D现实世界。我们对多个3D任务进行评估，发现我们的PSF与标准扩散模型相当，表现优于其他有效的3D点云生成方法。在现实世界中的应用程序中，例如Point Cloud完成和低延迟设置中的无训练文本引导生成，PSF表现出色。
Diffusion models have emerged as a powerful tool for point cloud generation. A key component that drives the impressive performance for generating high-quality samples from noise is iteratively denoise for thousands of steps. While beneficial, the complexity of learning steps has limited its applications to many 3D real-world. To address this limitation, we propose Point Straight Flow (PSF), a model that exhibits impressive performance using one step. Our idea is based on the reformulation of the standard diffusion model, which optimizes the curvy learning trajectory into a straight path. Further, we develop a distillation strategy to shorten the straight path into one step without a performance loss, enabling applications to 3D real-world with latency constraints. We perform evaluations on multiple 3D tasks and find that our PSF performs comparably to the standard diffusion model, outperforming other efficient 3D point cloud generation methods. On real-world applications such as point cloud completion and training-free text-guided generation in a low-latency setup, PSF performs favorably.
