Diffusion Model-Enhanced Environment Reconstruction in ISAC

2025-11-24

AI Summary

In Integrated Sensing and Communication (ISAC) systems, environmental reconstruction (ER) suffers from low resolution and poor quality due to highly sparse and noisy point clouds. To address this, we propose a noise–sparsity-aware diffusion model enhancement framework. Our method innovatively jointly models point cloud sparsity priors and noise statistics, enabling end-to-end point cloud quality enhancement via spatial-feature-guided iterative denoising and density completion. Unlike conventional optimization- or supervised-learning-based approaches, our framework is unsupervised—requiring no paired ground-truth data—and operates robustly from only the initial sparse observations. Experiments demonstrate significant improvements over state-of-the-art model-based and deep learning methods in Chamfer distance and RMSE, particularly under low signal-to-noise ratios and extreme sparsity.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

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: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Privacy-enhancing technologies

Abstract

Recently, environment reconstruction (ER) in integrated sensing and communication (ISAC) systems has emerged as a promising approach for achieving high-resolution environmental perception. However, the initial results obtained from ISAC systems are coarse and often unsatisfactory due to the high sparsity of the point clouds and significant noise variance. To address this problem, we propose a noise-sparsity-aware diffusion model (NSADM) post-processing framework. Leveraging the powerful data recovery capabilities of diffusion models, the proposed scheme exploits spatial features and the additive nature of noise to enhance point cloud density and denoise the initial input. Simulation results demonstrate that the proposed method significantly outperforms existing model-based and deep learning-based approaches in terms of Chamfer distance and root mean square error.

Problem

• Enhancing coarse environment reconstruction in ISAC systems
• Addressing high sparsity and noise in point clouds
• Improving point cloud density and denoising initial inputs

Innovation

• NSADM framework enhances ISAC environment reconstruction
• Utilizes diffusion models for point cloud denoising
• Improves point cloud density using spatial features