We present a novel method for accurate and efficient up- sampling of sparse depth data, guided by high-resolution imagery. Our approach goes beyond the use of intensity cues only and additionally exploits object boundary cues through structured edge detection and semantic scene labeling for guidance. Both cues are combined within a geodesic distance measure that allows for boundary-preserving depth in- terpolation while utilizing local context. We model the observed scene structure by locally planar elements and formulate the upsampling task as a global energy minimization problem. Our method determines glob- ally consistent solutions and preserves fine details and sharp depth bound- aries. In our experiments on several public datasets at different levels of application, we demonstrate superior performance of our approach over the state-of-the-art, even for very sparse measurements.

本研究提出了一种全新的、通过高分辨率图像引导的、精确高效的稀疏深度数据上采样方法，使用结构化边缘检测和语义场景标记来获取目标边缘线索，并将两种线索结合于测地距离度量中，进而在利用局部内容的情况下，实现了保持边界的深度插值，通过将观察到的场景结构建模为本地平面元素，并将上采样任务形式化为全局能量最小化问题，定出全局一致的解决方案，有效地保留了细节和深度边界，实验结果表明，与现有技术相比，本研究方法在不同的应用水平下均表现出卓越的性能。

语义引导深度上采样