Collaborative perception has garnered significant attention for its ability to enhance the perception capabilities of individual vehicles through the exchange of information with surrounding vehicle-agents. However, existing collaborative perception systems are limited by inefficiencies in user interaction and the challenge of multi-camera photorealistic visualization. To address these challenges, this paper introduces ChatStitch, the first collaborative perception system capable of unveiling obscured blind spot information through natural language commands integrated with external digital assets. To adeptly handle complex or abstract commands, ChatStitch employs a multi-agent collaborative framework based on Large Language Models. For achieving the most intuitive perception for humans, ChatStitch proposes SV-UDIS, the first surround-view unsupervised deep image stitching method under the non-global-overlapping condition. We conducted extensive experiments on the UDIS-D, MCOV-SLAM open datasets, and our real-world dataset. Specifically, our SV-UDIS method achieves state-of-the-art performance on the UDIS-D dataset for 3, 4, and 5 image stitching tasks, with PSNR improvements of 9%, 17%, and 21%, and SSIM improvements of 8%, 18%, and 26%, respectively.
View on arXiv@article{liang2025_2503.14948,
title={ ChatStitch: Visualizing Through Structures via Surround-View Unsupervised Deep Image Stitching with Collaborative LLM-Agents },
author={ Hao Liang and Zhipeng Dong and Yi Yang and Mengyin Fu },
journal={arXiv preprint arXiv:2503.14948},
year={ 2025 }
}