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diffusion-based point cloud

A diffusion-based point cloud refers to a three-dimensional geometric representation generated, reconstructed, or processed through diffusion probabilistic models. In this framework, a generative neural network learns the underlying spatial distribution of 3D coordinates by reversing a diffusion process that progressively corrupts structured point sets with Gaussian noise. Starting from pure noise or conditioning inputs such as 2D images, text prompts, or partial scans, the model iteratively predicts and removes noise across continuous or discrete steps to produce accurate, unordered sets of surface points. This approach enables the synthesis of high-fidelity, complex 3D shapes while maintaining permutation invariance, and it is widely applied in tasks such as 3D shape generation, point cloud completion, denoising, and upsampling.

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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