Augmented reality (AR) using camera images in mobile devices is becoming popular for tourism promotion. However, obstructions such as tourists appearing in the camera images may cause the camera pose estimation error, resulting in CG misalignment and reduced visibility of the contents. To avoid this problem, Indirect AR (IAR), which does not use real-time camera images, has been proposed. In this method, an omnidirectional image is captured and virtual objects are synthesized on the image in advance. Users can experience AR by viewing a scene extracted from the synthesized omnidirectional image according to the device's sensor. This enables robustness and high visibility. However, if the weather conditions and season in the pre-captured 360 images differs from the current weather conditions and season when AR is experienced, the realism of the AR experience is reduced. To overcome the problem, we propose a method for correcting the intensity and texture of a past omnidirectional image using camera images from mobile devices. We first perform semantic segmentation. We then reproduce the current sky pattern by panoramic image composition and inpainting. For the other areas, we correct the intensity by histogram matching. In experiments, we show the effectiveness of the proposed method using various scenes.

翻译：基于移动设备相机图像的增强现实（AR）技术在旅游推广领域日益普及。然而，相机图像中出现的游客等遮挡物可能导致相机姿态估计误差，进而引发计算机图形错位与内容可视性下降。为规避此问题，学界提出了不依赖实时相机图像的间接增强现实（IAR）方法。该方法通过预先采集全景图像并在图像上合成虚拟对象，用户可根据设备传感器从合成全景图像中提取场景进行AR体验，从而实现系统的鲁棒性与高可视性。但若预先采集的360度图像与当前AR体验时的天气、季节条件存在差异，将削弱AR体验的真实感。为解决该问题，本文提出一种利用移动设备相机图像校正历史全景图像强度与纹理的方法。首先进行语义分割，继而通过全景图像合成与修复技术重建当前天空模式，其余区域则采用直方图匹配进行强度校正。实验部分通过多场景验证了所提方法的有效性。