We propose an unsupervised real-time dense depth completion from a sparse depth map guided by a single image. Our method generates a smooth depth map while preserving discontinuity between different objects. Our key idea is a Binary Anisotropic Diffusion Tensor (B-ADT) which can completely eliminate smoothness constraint at intended positions and directions by applying it to variational regularization. We also propose an Image-guided Nearest Neighbor Search (IGNNS) to derive a piecewise constant depth map which is used for B-ADT derivation and in the data term of the variational energy. Our experiments show that our method can outperform previous unsupervised and semi-supervised depth completion methods in terms of accuracy. Moreover, since our resulting depth map preserves the discontinuity between objects, the result can be converted to a visually plausible point cloud. This is remarkable since previous methods generate unnatural surface-like artifacts between discontinuous objects.

中文翻译：

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使用二元各向异性扩散张量完成不连续且平滑的深度补全

我们从单个图像引导的稀疏深度图提出了一种无监督的实时密集深度补全。我们的方法生成平滑的深度图，同时保留不同对象之间的不连续性。我们的关键思想是二元各向异性扩散张量 (B-ADT)，它可以通过将其应用于变分正则化来完全消除预期位置和方向上的平滑约束。我们还提出了一种图像引导的最近邻搜索 (IGNNS) 来推导分段恒定深度图，该图用于 B-ADT 推导和变分能量的数据项。我们的实验表明，我们的方法在准确性方面可以优于以前的无监督和半监督深度补全方法。此外，由于我们生成的深度图保留了对象之间的不连续性，结果可以转换为视觉上合理的点云。这是非常了不起的，因为以前的方法会在不连续的对象之间产生不自然的类表面伪影。