College of Computer Science, Sichuan University, PR China.
IEEE Trans Vis Comput Graph. 2012 Apr;18(4):573-80. doi: 10.1109/TVCG.2012.53.
In augmented reality, one of key tasks to achieve a convincing visual appearance consistency between virtual objects and video scenes is to have a coherent illumination along the whole sequence. As outdoor illumination is largely dependent on the weather, the lighting condition may change from frame to frame. In this paper, we propose a full image-based approach for online tracking of outdoor illumination variations from videos captured with moving cameras. Our key idea is to estimate the relative intensities of sunlight and skylight via a sparse set of planar feature-points extracted from each frame. To address the inevitable feature misalignments, a set of constraints are introduced to select the most reliable ones. Exploiting the spatial and temporal coherence of illumination, the relative intensities of sunlight and skylight are finally estimated by using an optimization process. We validate our technique on a set of real-life videos and show that the results with our estimations are visually coherent along the video sequences.
在增强现实中，实现虚拟对象和视频场景之间令人信服的视觉外观一致性的关键任务之一是在整个序列中保持连贯的照明。由于户外照明在很大程度上取决于天气，因此照明条件可能会逐帧变化。在本文中，我们提出了一种基于全图像的方法，用于从移动摄像机拍摄的视频中在线跟踪户外照明变化。我们的主要思想是通过从每一帧中提取的稀疏平面特征点来估计阳光和天空光的相对强度。为了解决不可避免的特征配准问题，引入了一组约束条件来选择最可靠的特征点。利用光照的空间和时间相干性，通过优化过程最终估计阳光和天空光的相对强度。我们在一组真实视频上验证了我们的技术，并表明使用我们的估计值在视频序列中具有视觉一致性。
