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Temporal Coherence in Image-Based Visual Hull Rendering

机译:基于图像的视觉船体渲染中的时间相干性

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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.
机译:基于图像的视觉船体渲染是一种用于从由校准相机捕获的剪影图像集中生成所需视点的深度图的方法。它不计算独立于视图的数据表示,例如体素网格或网格,这使其对于动态场景特别有效。捕获用户时,场景通常是动态的,但不会快速变化,因为人们会在亚秒级的时间内平稳移动。由于缺乏明确的数据表示,因此利用这种时间上的一致性来避免重复的计算具有挑战性。本文分析了基于图像的视觉船体算法,以找到随时间推移保持有效的中间信息,因此值得加以明确。然后,我们得出利用这些信息来改善渲染性能的方法。我们的方法最多可将执行时间减少25%。当用户的动作非常缓慢时,可减少多达50%的运动。

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