SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization

2025-03-18

AI Summary

Point clouds often exhibit coupled degradations—including incompleteness, low resolution, noise, and absence of color—yet existing methods typically address these defects independently in isolated single-task frameworks, leading to error accumulation and computational redundancy when cascaded. This paper introduces SuperPC, the first unified diffusion model enabling end-to-end joint restoration for point cloud completion, upsampling, denoising, and coloring. Its core is a three-level conditional diffusion framework: (i) multi-stage conditional control injects task-specific priors; (ii) a spatially mixed fusion mechanism jointly models geometric and chromatic features; and (iii) joint noise prediction coupled with cross-task feature decoupling enhances representational fidelity. SuperPC consistently outperforms state-of-the-art specialized models and cascaded baselines across all four tasks, achieving significant gains in both accuracy and efficiency while demonstrating strong generalization to real-world applications such as autonomous driving and 3D reconstruction.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Point cloud (PC) processing tasks-such as completion, upsampling, denoising, and colorization-are crucial in applications like autonomous driving and 3D reconstruction. Despite substantial advancements, prior approaches often address each of these tasks independently, with separate models focused on individual issues. However, this isolated approach fails to account for the fact that defects like incompleteness, low resolution, noise, and lack of color frequently coexist, with each defect influencing and correlating with the others. Simply applying these models sequentially can lead to error accumulation from each model, along with increased computational costs. To address these challenges, we introduce SuperPC, the first unified diffusion model capable of concurrently handling all four tasks. Our approach employs a three-level-conditioned diffusion framework, enhanced by a novel spatial-mix-fusion strategy, to leverage the correlations among these four defects for simultaneous, efficient processing. We show that SuperPC outperforms the state-of-the-art specialized models as well as their combination on all four individual tasks.

Problem

• Unified model for point cloud tasks
• Handles completion, upsampling, denoising, colorization
• Reduces error accumulation and computational costs

Innovation

• Unified diffusion model for multiple tasks
• Three-level-conditioned diffusion framework
• Spatial-mix-fusion strategy for defect correlation