手机拍的和屏幕看的终于能一样了
你手机拍完照片，屏幕上一看，颜色总是不对劲——要么偏黄，要么太亮。这不是你手机差，而是行业惯例把摄像头和屏幕分开校准，中间信息丢失严重。这篇论文反其道而行：把摄像头和屏幕当成一个整体系统来训练AI，让AI直接学习从真实场景到屏幕显示的全链路映射。结果用户评分提升2分（满分5），量化指标翻倍。它不是你明天就能用的功能，但指明了手机厂商解决色差问题的新方向——以后拍完即所见，不再需要手动调色。
📄 原文摘要(英文)
When a real-world scene is captured by a smartphone camera and viewed on its screen, the displayed image often differs noticeably from the original scene in color, brightness, and contrast. This gap persists despite substantial advances in both modern cameras and displays. A key reason is that most pipelines factor the high-dimensional capture-to-display process into two separately calibrated camera and display stages, and then connect them through low-dimensional color transforms, leading to information bottlenecks and inevitable error accumulation. To address this systemic challenge, we propose Color Pass-Through, an end-to-end learned framework that operates directly on captured images. Our key insight is to treat the camera and display as a coupled system rather than calibrating them in isolation. Coupling the camera and display yields two practical advantages: (1) it brings the entire real-world scenes to the display via end-to-end optimization, and (2) it allows efficient one-step calibration for each distinct observer via complete capture-to-display path. We validate Color Pass-Through using both digital and human observers. Compared with representative baselines, our method achieves an average gain of +2.0 points on a 5-point user study and more than 2x improvement on quantitative metrics, demonstrating improved reproduction of the perceived color of the original scene.