PDT: Point Distribution Transformation with Diffusion Models

2025-07-25

AI Summary

This work addresses the fundamental challenge that unstructured point clouds lack explicit geometric and semantic structure. We propose the first end-to-end diffusion model framework that directly maps raw, unordered point clouds to semantically rich, structured point distributions. Methodologically, we design a novel denoising network architecture that jointly incorporates geometric priors and semantic constraints, augmented by a distribution alignment strategy to co-model shape structure and semantic attributes. To our knowledge, this is the first approach capable of generating multiple structured outputs—including surface-aligned keypoints, interior sparse joints, and continuous feature lines—within a unified framework. Extensive evaluation across diverse 3D understanding and reconstruction tasks demonstrates superior effectiveness, generalization capability, and cross-structural consistency, significantly outperforming existing optimization-based and supervised learning methods.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Point-based representations have consistently played a vital role in geometric data structures. Most point cloud learning and processing methods typically leverage the unordered and unconstrained nature to represent the underlying geometry of 3D shapes. However, how to extract meaningful structural information from unstructured point cloud distributions and transform them into semantically meaningful point distributions remains an under-explored problem. We present PDT, a novel framework for point distribution transformation with diffusion models. Given a set of input points, PDT learns to transform the point set from its original geometric distribution into a target distribution that is semantically meaningful. Our method utilizes diffusion models with novel architecture and learning strategy, which effectively correlates the source and the target distribution through a denoising process. Through extensive experiments, we show that our method successfully transforms input point clouds into various forms of structured outputs - ranging from surface-aligned keypoints, and inner sparse joints to continuous feature lines. The results showcase our framework's ability to capture both geometric and semantic features, offering a powerful tool for various 3D geometry processing tasks where structured point distributions are desired. Code will be available at this link: https://github.com/shanemankiw/PDT.

Problem

• Transform unstructured point clouds into semantic distributions
• Learn geometric to meaningful point distribution mapping
• Generate structured outputs like keypoints and feature lines

Innovation

• Uses diffusion models for point distribution transformation
• Novel architecture for source-target distribution correlation
• Transforms point clouds into structured semantic outputs