论文标题
SSGP：稀疏的空间引导传播，用于鲁棒和通用插值
SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation
论文作者
论文摘要
稀疏像素信息插入密集的目标分辨率的插值在计算机视觉中发现了其在多个学科的应用。运动场的最新插值应用基于模型的插值，可利用从目标图像中提取的边缘信息。为了完成深度，数据驱动的学习方法是广泛的。我们的工作灵感来自最新趋势，以解决稀疏信息的密集指导问题。我们扩展了这些想法，并创建了通用的跨域体系结构，该体系结构可用于多种插值问题，例如光流，场景流或深度完成。在我们的实验中，我们表明，与专业算法相比，我们提出的稀疏空间引导传播（SSGP）的概念可以提高鲁棒性，准确性或速度。
Interpolation of sparse pixel information towards a dense target resolution finds its application across multiple disciplines in computer vision. State-of-the-art interpolation of motion fields applies model-based interpolation that makes use of edge information extracted from the target image. For depth completion, data-driven learning approaches are widespread. Our work is inspired by latest trends in depth completion that tackle the problem of dense guidance for sparse information. We extend these ideas and create a generic cross-domain architecture that can be applied for a multitude of interpolation problems like optical flow, scene flow, or depth completion. In our experiments, we show that our proposed concept of Sparse Spatial Guided Propagation (SSGP) achieves improvements to robustness, accuracy, or speed compared to specialized algorithms.
