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Fast Point Cloud Generation

Fast point cloud generation refers to the process of rapidly synthesizing three-dimensional point cloud data from random noise or conditional inputs with minimal computational latency and reduced sampling steps. In 3D computer vision and generative modeling, standard frameworks such as diffusion models often rely on iterative multi-step refinement to produce accurate geometric representations, which can restrict their use in time-critical environments. Fast point cloud generation addresses this bottleneck by employing accelerated sampling algorithms, model distillation, and streamlined generative trajectories that compress the synthesis process into few or single steps without substantial loss of structural fidelity. This efficiency is critical for deploying 3D generative capabilities in real-time applications such as robotics, autonomous driving, augmented reality, and interactive spatial computing.

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Fast Point Cloud Generation with Straight Flows

Fast Point Cloud Generation with Straight Flows

Lemeng Wu, Dilin Wang, Chengyue Gong, Xingchao Liu, Yunyang Xiong, Rakesh Ranjan, Raghuraman Krishnamoorthi, Vikas Chandra, Qiang Liu

OrganizationsMetaUniversity of Texas at Austin

Why you should read this

Proposes Point Straight Flow, a novel framework that straightens generative transport trajectories and distills them into a single step, enabling high-quality 3D point cloud generation over 700 times faster than standard diffusion models.

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.

Added

2026-09-26