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Choose your own viewpoint: A high-quality/low-complexity free-viewpoint 3D visual system

机译:选择您自己的视点:高质量/低复杂度的自由视点3D视觉系统

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Choosing one's own viewpoint when watching a video program has long been a desire for viewers. To achieve this goal, view synthesis and depth map generation are two fundamental techniques. View synthesis is a signal processing procedure which creates dense virtual views based on sparse real views. Each object inside a frame is warped to a proper position according to its depth information to form the viewpoint changing perception for viewers. Hence, the correctness of depth map influences the view synthesis quality. To increase the accuracy of depth map, this paper proposes an edge-adaptive block matching scheme cooperated with an unreliable region repairing approach. The former avoid finding local minimum in stereo matching, and the latter repairs the errors caused by occlusion regions. As for view synthesis, this paper proposes a special warping method that can detect errors caused by boundary mismatches of objects between corresponding depth and color images to improve quality of the synthesized view. Besides, we also propose a compensative-filling method that can fix tiny cracks due to round-off errors. Because of these two features, the proposed view synthesis becomes more robust to tolerate errors inside depth maps when compared with previous schemes. Both the depth generation and view synthesis are extremely complex computations. Therefore, this paper also proposes a low-complexity computing technology based on group-of-pixels which increases 30 times of performance for depth map generation, and reduces 60% computation time of view synthesis.
机译:观看视频节目时,选择自己的观点一直是观众的愿望。为了实现此目标,视图合成和深度图生成是两项基本技术。视图合成是一种信号处理过程,可基于稀疏的真实视图创建密集的虚拟视图。框内的每个对象根据其深度信息被扭曲到适当的位置,以形成观看者改变视点的感知。因此,深度图的正确性会影响视图合成质量。为了提高深度图的精度,提出了一种边缘匹配的块匹配方案,并结合了不可靠的区域修复方法。前者避免在立体声匹配中找到局部最小值,而后者则修复了由遮挡区域引起的误差。对于视图合成,本文提出了一种特殊的变形方法,该方法可以检测由相应深度和彩色图像之间的对象的边界不匹配引起的误差,以提高合成视图的质量。此外,我们还提出了一种补偿式填充方法,该方法可以修复由于舍入误差而导致的细小裂纹。由于这两个特征,与以前的方案相比,所提出的视图合成变得更加健壮,可以容忍深度图内部的错误。深度生成和视图合成都是极其复杂的计算。因此,本文还提出了一种基于像素组的低复杂度计算技术,该技术将深度图生成的性能提高了30倍,并减少了60%的视图合成计算时间。

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