Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, A-8010 Graz, Austria.
IEEE Trans Vis Comput Graph. 2013 Oct;19(10):1758-67. doi: 10.1109/TVCG.2013.85.
Image-based visual hull rendering is a method for generating depth maps of a desired viewpoint from a set of silhouette images captured by calibrated cameras. It does not compute a view-independent data representation, such as a voxel grid or a mesh, which makes it particularly efficient for dynamic scenes. When users are captured, the scene is usually dynamic, but does not change rapidly because people move smoothly within a subsecond time frame. Exploiting this temporal coherence to avoid redundant calculations is challenging because of the lack of an explicit data representation. This paper analyzes the image-based visual hull algorithm to find intermediate information that stays valid over time and is, therefore, worth to make explicit. We then derive methods that exploit this information to improve the rendering performance. Our methods reduce the execution time by up to 25 percent. When the user's motions are very slow, reductions of up to 50 percent are achieved.
基于图像的视觉体渲染是一种从一组通过校准相机捕获的轮廓图像生成期望视点的深度图的方法。它不计算视图独立的数据表示形式,例如体素网格或网格,这使得它对于动态场景特别有效。当捕获用户时,场景通常是动态的,但不会因为人在亚秒时间范围内平滑移动而快速变化。由于缺乏显式数据表示形式,利用这种时间一致性来避免冗余计算具有挑战性。本文分析了基于图像的视觉体算法,以找到随时间保持有效且因此值得显式表示的中间信息。然后,我们推导出利用这些信息来提高渲染性能的方法。我们的方法将执行时间缩短了 25%。当用户的运动非常缓慢时,可实现高达 50%的减少。