Department of Computer Science, University of Kentucky, 329 Rose Street, Lexington, KY 40506, USA.
IEEE Trans Pattern Anal Mach Intell. 2013 Oct;35(10):2526-38. doi: 10.1109/TPAMI.2013.55.
We describe algorithms that use cloud shadows as a form of stochastically structured light to support 3D scene geometry estimation. Taking video captured from a static outdoor camera as input, we use the relationship of the time series of intensity values between pairs of pixels as the primary input to our algorithms. We describe two cues that relate the 3D distance between a pair of points to the pair of intensity time series. The first cue results from the fact that two pixels that are nearby in the world are more likely to be under a cloud at the same time than two distant points. We describe methods for using this cue to estimate focal length and scene structure. The second cue is based on the motion of cloud shadows across the scene; this cue results in a set of linear constraints on scene structure. These constraints have an inherent ambiguity, which we show how to overcome by combining the cloud motion cue with the spatial cue. We evaluate our method on several time lapses of real outdoor scenes.
我们描述了一些算法，这些算法利用云影作为一种随机结构光的形式来支持 3D 场景几何估计。我们以从静态户外摄像机捕获的视频作为输入，使用像素对之间的强度值时间序列之间的关系作为我们算法的主要输入。我们描述了两个线索，它们将一对点之间的 3D 距离与一对强度时间序列联系起来。第一个线索是基于这样一个事实，即在世界上附近的两个像素同时处于云层下的可能性大于两个远距离点。我们描述了使用此线索来估计焦距和场景结构的方法。第二个线索基于云影在场景中的运动；这个线索导致了对场景结构的一组线性约束。这些约束具有固有的歧义性，我们通过将云运动线索与空间线索相结合来展示如何克服这些歧义性。我们在几个真实户外场景的时间推移上评估了我们的方法。
