# Diffusion Model-Enhanced Environment Reconstruction in ISAC

arXiv: https://arxiv.org/abs/2511.19044 · HTML: https://mlsanity.com/paper/2511.19044/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2025.

## Built on by (most cited first, 2 of 2)

- [CRB-Guided Framework Design and Resource Allocation for Indoor mmWave ISCC Systems](https://mlsanity.com/paper/2605.29939/) (2026)
- [Agentic AI Networking for Heterogeneous Unmanned Aerial Systems in Low-Altitude Wireless Networks](https://mlsanity.com/paper/2609.19538/) (2026)

## Abstract (arXiv)

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.