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Variational image-based rendering with gradient constraints

机译:基于变分图像的渲染梯度约束

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Multi-view image-based rendering consists in generating a novel view of a scene from a set of source views. In general, this works by first doing a coarse 3D reconstruction of the scene, and then using this reconstruction to establish correspondences between source and target views, followed by blending the warped views to get the final image. Unfortunately, discontinuities in the blending weights, due to scene geometry or camera placement, result in artifacts in the target view. In this paper, we show how to avoid these artifacts by imposing additional constraints on the image gradients of the novel view. We propose a variational framework in which an energy functional is derived and optimized by iteratively solving a linear system. We demonstrate this method on several structured and unstructured multi-view datasets, and show that it numerically outperforms state-of-the-art methods, and eliminates artifacts that result from visibility discontinuities.
机译:基于多视图的基于图像的渲染包括从一组源视图生成场景的新颖视图。通常,这首先通过首先进行粗略的3D重建场景,然后使用这种重建来建立源和目标视图之间的对应关系,然后将扭曲视图混合以获得最终图像。不幸的是,由于场景几何或摄像机放置,在混合权重中的不连续性导致目标视图中的伪像。在本文中,我们展示了如何通过对新颖视图的图像梯度施加额外的约束来避免这些伪影。我们提出了一种变分框架,其中通过迭代求解线性系统来推导和优化能量功能。我们在几个结构化和非结构化的多视图数据集上演示了这种方法,并表明它以数字方式优于最先进的方法,并消除了来自可见性不连续性的伪像。

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