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首页> 外文会议>Stereoscopic Displays and Virtual Reality Systems XIV; Proceedings of SPIE-The International Society for Optical Engineering; vol.6490; Electronic Imaging Science and Technology >Spline-based intermediate view reconstruction
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Spline-based intermediate view reconstruction

机译：基于样条的中间视图重建

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摘要

Intermediate view reconstruction is an essential step in content preparation for multiview 3D displays and free-viewpoint video. Although many approaches to view reconstruction have been proposed to date, most of them share the need to model and estimate scene depth first, and follow with the estimation of unknown-view texture using this depth and other views. The approach we present in this paper follows this path as well. First, assuming a reliable disparity (depth) map is known between two views, we present a spline-based approach to unknown-view texture estimation, and compare its performance with standard disparity-compensated interpolation. A distinguishing feature of the spline-based reconstruction is that all virtual views between the two known views can be reconstructed from a single disparity field, unlike in disparity-compensated interpolation. In the second part of the paper, we concentrate on the recovery of reliable disparities especially at object boundaries. We outline an occlusion-aware disparity estimation method that we recently proposed; it jointly computes disparities in visible areas, inpaints disparities in occluded areas and implicitly detects occlusion areas. We then show how to combine occlusion-aware disparity estimation with spline-based view reconstruction presented earlier, and we experimentally demonstrate its benefits compared to occlusion-unaware disparity-compensated interpolation.
机译：在多视图3D显示器和自由视点视频的内容准备中，中间视图重建是必不可少的步骤。尽管迄今为止已经提出了许多用于视图重建的方法，但是大多数方法都需要首先建模和估计场景深度，然后再使用该深度和其他视图来估计未知视图纹理。我们在本文中介绍的方法也遵循此路径。首先，假设在两个视图之间已知可靠的视差（深度）图，我们将提出一种基于样条的未知视图纹理估计方法，并将其性能与标准视差补偿插值进行比较。基于样条的重建的一个显着特征是，可以从单个视差字段重建两个已知视图之间的所有虚拟视图，这与视差补偿插值不同。在本文的第二部分中，我们将重点放在可靠视差的恢复上，尤其是在对象边界处。我们概述了我们最近提出的一种可识别咬合的视差估计方法。它联合计算可见区域中的视差，在遮挡区域中修补视差，并隐式检测遮挡区域。然后，我们展示了如何将遮挡感知的视差估计与之前介绍的基于样条的视图重建相结合，并通过实验证明了其与遮挡感知的视差补偿插值相比的优势。

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