Multiview depth imagery will play a critical role in free-viewpoint television. This technology requires high quality virtual view synthesis to enable viewers to move freely in a dynamic real world scene. Depth imagery at different viewpoints is used to synthesize an arbitrary number of novel views. Usually, depth images at multiple viewpoints are estimated individually by stereo-matching algorithms, and hence, show lack of interview consistency. This inconsistency affects the quality of view synthesis negatively. This paper proposes a method for depth consistency testing in depth difference subspace to enhance the depth representation of a scene across multiple viewpoints. Furthermore, we propose a view synthesis algorithm that uses the obtained consistency information to improve the visual quality of virtual views at arbitrary viewpoints. Our method helps us to find a linear subspace for our depth difference measurements in which we can test the inter-view consistency efficiently. With this, our approach is able to enhance the depth information for real world scenes. In combination with our consistency-adaptive view synthesis, we improve the visual experience of the free-viewpoint user. The experiments show that our approach enhances the objective quality of virtual views by up to 1.4 dB. The advantage for the subjective quality is also demonstrated.

翻译：多视角深度图像在自由视点电视中扮演关键角色。该技术需要高质量虚拟视点合成，使观众能在动态真实场景中自由移动。不同视角的深度图像用于合成任意数量的新视点。通常，多视角深度图像通过立体匹配算法独立估计，因此缺乏视角间一致性。这种不一致性对合成质量产生负面影响。本文提出一种在视差深度子空间中进行深度一致性检验的方法，以增强场景跨多视角的深度表征。此外，我们提出一种利用所获一致性信息提升任意视点虚拟视点图像质量的合成算法。该方法有助于找到深度差异测量的线性子空间，可在其中高效检验视角间一致性。由此，该方法能增强真实场景的深度信息。结合一致性自适应视点合成，我们改善了自由视点用户的视觉体验。实验表明，该方法将虚拟视点客观质量提升高达1.4 dB，主观质量优势也得到了验证。