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Diffusion models have emerged as a powerful tool for point cloud generation.A key component that drives the impressive performance for generatinghigh-quality samples from noise is iteratively denoise for thousands of steps.While beneficial, the complexity of learning steps has limited its applicationsto many 3D real-world. To address this limitation, we propose Point StraightFlow (PSF), a model that exhibits impressive performance using one step. Ouridea is based on the reformulation of the standard diffusion model, whichoptimizes the curvy learning trajectory into a straight path. Further, wedevelop a distillation strategy to shorten the straight path into one stepwithout a performance loss, enabling applications to 3D real-world with latencyconstraints. We perform evaluations on multiple 3D tasks and find that our PSFperforms comparably to the standard diffusion model, outperforming otherefficient 3D point cloud generation methods. On real-world applications such aspoint cloud completion and training-free text-guided generation in alow-latency setup, PSF performs favorably.

Fast Point Cloud Generation with Straight Flows