Depth refinement on sparse-depth images using visual perception cues

Muhammad Umar Karim Khan, Asim Khan, C. Kyung
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Abstract

Numerous depth extraction schemes cannot extract depth on textureless regions, thus generating sparse depth maps. In this paper, we propose using perception cues to improve the sparse depth map. We consider the local neighborhood as well the global surface properties of objects. We use this information to complement depth extraction schemes. The method is not scene or class specific. With quantitative evaluation, the proposed method is shown to perform better compared to previous depth refinement methods. The error in terms of standard deviation of depth has been reduced down by 60%. The computational overhead of the proposed method is also very low, making it a suitable candidate for depth refinement.
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利用视觉感知线索对稀疏深度图像进行深度细化
许多深度提取方案无法在无纹理区域上提取深度,从而产生稀疏的深度图。在本文中,我们提出使用感知线索来改进稀疏深度图。我们考虑了物体的局部邻域和全局表面性质。我们使用这些信息来补充深度提取方案。该方法不是特定于场景或类的。定量评价表明,该方法比以往的深度细化方法具有更好的性能。深度标准偏差的误差降低了60%。所提出的方法的计算开销也非常低,使其成为深度细化的合适候选。
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