轻量级色彩融合算法，让AR设备实时生成自然贴合的合成图像。
Lightweight Optimal-Transport Harmonization on Edge Devices
- 用紧凑编码器预测最优传输映射，实现边缘设备部署。
- 在真实AR合成图像上综合得分领先现有方法。
- 开源专用数据集与采集工具，助力后续研究。
色彩谐调通过调整插入物体的颜色，使其在视觉上与周围图像融合，实现无缝拼接，是增强现实（AR）中的核心问题。然而，当前谐调算法尚未融入AR流程，主要因缺乏实时解决方案。本文提出轻量级的MKL-Harmonizer算法，基于经典最优传输理论，训练小型编码器以预测Monge-Kantorovich传输映射，支持设备端实时推理。我们在真实合成的AR图像上进行评估，结果表明该方法在综合评分上优于现有最先进方法。同时，我们发布了包含像素级掩码的专用AR合成图像数据集及数据采集工具包，以支持研究者进一步获取数据。
原文摘要 · Abstract (English)
Color harmonization adjusts the colors of an inserted object so that it perceptually matches the surrounding image, resulting in a seamless composite. The harmonization problem naturally arises in augmented reality (AR), yet harmonization algorithms are not currently integrated into AR pipelines because real-time solutions are scarce. In this work, we address color harmonization for AR by proposing a lightweight approach that supports on-device inference. For this, we leverage classical optimal transport theory by training a compact encoder to predict the Monge-Kantorovich transport map. We benchmark our MKL-Harmonizer algorithm against state-of-the-art methods and demonstrate that for real composite AR images our method achieves the best aggregated score. We release our dedicated AR dataset of composite images with pixel-accurate masks and data-gathering toolkit to support further data acquisition by researchers.
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