Modification Takes Courage: Seamless Image Stitching via Reference-Driven Inpainting

• URL: http://arxiv.org/abs/2411.10309v1
• Date: Fri, 15 Nov 2024 16:05:01 GMT
• Title: Modification Takes Courage: Seamless Image Stitching via Reference-Driven Inpainting
• Authors: Ziqi Xie, Xiao Lai, Weidong Zhao, Xianhui Liu, Wenlong Hou,
• Abstract summary: Current image stitching methods produce noticeable seams in challenging scenarios such as uneven hue and large parallax. We propose the Reference-Driven Inpainting Stitcher (RDIStitcher) to reformulate the image fusion and rectangling as a reference-based inpainting model. We present the Multimodal Large Language Models (MLLMs)-based metrics, offering a new perspective on evaluating stitched image quality.
• Score: 0.17975553762582286
• License:
• Abstract: Current image stitching methods often produce noticeable seams in challenging scenarios such as uneven hue and large parallax. To tackle this problem, we propose the Reference-Driven Inpainting Stitcher (RDIStitcher), which reformulates the image fusion and rectangling as a reference-based inpainting model, incorporating a larger modification fusion area and stronger modification intensity than previous methods. Furthermore, we introduce a self-supervised model training method, which enables the implementation of RDIStitcher without requiring labeled data by fine-tuning a Text-to-Image (T2I) diffusion model. Recognizing difficulties in assessing the quality of stitched images, we present the Multimodal Large Language Models (MLLMs)-based metrics, offering a new perspective on evaluating stitched image quality. Compared to the state-of-the-art (SOTA) method, extensive experiments demonstrate that our method significantly enhances content coherence and seamless transitions in the stitched images. Especially in the zero-shot experiments, our method exhibits strong generalization capabilities. Code: https://github.com/yayoyo66/RDIStitcher

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