ChatStitch: Visualizing Through Structures via Surround-View Unsupervised Deep Image Stitching With Collaborative LLM-Agents

TY - JOUR

T1 - ChatStitch

T2 - Visualizing Through Structures via Surround-View Unsupervised Deep Image Stitching With Collaborative LLM-Agents

AU - Liang, Hao

AU - Dong, Zhipeng

AU - Chen, Kaixin

AU - Li, Hao

AU - Guo, Jiyuan

AU - Yue, Yufeng

AU - Fu, Mengyin

AU - Yang, Yi

N1 - Publisher Copyright: © 2025 IEEE.

PY - 2026

Y1 - 2026

N2 - Surround-view perception has garnered significant attention for its ability to enhance the perception capabilities of autonomous driving vehicles through the exchange of information with surrounding cameras. However, existing surround-view perception systems are limited by inefficiencies in unidirectional interaction pattern with human and distortions in overlapping regions exponentially propagating into non-overlapping areas. To address these challenges, this paper introduces ChatStitch, a surround-view human-machine co-perception system capable of unveiling obscured blind spot information through natural language commands integrated with external digital assets. To dismantle the unidirectional interaction bottleneck, ChatStitch implements a cognitively grounded closed-loop interaction multi-agent framework based on Large Language Models. To suppress distortion propagation across overlapping boundaries, ChatStitch proposes SV-UDIS, a 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. The code is available at https://github.com/lhlawrence/ChatStitch

AB - Surround-view perception has garnered significant attention for its ability to enhance the perception capabilities of autonomous driving vehicles through the exchange of information with surrounding cameras. However, existing surround-view perception systems are limited by inefficiencies in unidirectional interaction pattern with human and distortions in overlapping regions exponentially propagating into non-overlapping areas. To address these challenges, this paper introduces ChatStitch, a surround-view human-machine co-perception system capable of unveiling obscured blind spot information through natural language commands integrated with external digital assets. To dismantle the unidirectional interaction bottleneck, ChatStitch implements a cognitively grounded closed-loop interaction multi-agent framework based on Large Language Models. To suppress distortion propagation across overlapping boundaries, ChatStitch proposes SV-UDIS, a 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. The code is available at https://github.com/lhlawrence/ChatStitch

KW - Surround view

KW - human-machine interaction

KW - image stitching

UR - https://www.scopus.com/pages/publications/105019556603

U2 - 10.1109/TCSVT.2025.3622736

DO - 10.1109/TCSVT.2025.3622736

M3 - Article

AN - SCOPUS:105019556603

SN - 1051-8215

VL - 36

SP - 3027

EP - 3040

JO - IEEE Transactions on Circuits and Systems for Video Technology

JF - IEEE Transactions on Circuits and Systems for Video Technology

IS - 3

ER -