论文标题
使用立体指导和对抗性学习的深度围栏估算
Deep Fence Estimation using Stereo Guidance and Adversarial Learning
论文作者
论文摘要
人们捕获了令人难忘的事件和展览的图像，这些事件和展览通常被钢丝网遮挡，被称为围栏。由于最初的栅栏细分困难，拆除围栏的最新工作的性能有限。这项工作旨在使用立体声图像对产生的新型围栏指南掩模（FM）准确细分围栏。该二进制引导面罩包含有关围栏结构的确定性提示，并作为深栅栏估计模型的附加输入。我们还引入了定向连通性损失（DCL），该连通性损失与对抗性损失一起用于精确检测细线。在现实世界情景下获得的实验结果证明了所提出的方法优于最先进的技术。
People capture memorable images of events and exhibits that are often occluded by a wire mesh loosely termed as fence. Recent works in removing fence have limited performance due to the difficulty in initial fence segmentation. This work aims to accurately segment fence using a novel fence guidance mask (FM) generated from stereo image pair. This binary guidance mask contains deterministic cues about the structure of fence and is given as additional input to the deep fence estimation model. We also introduce a directional connectivity loss (DCL), which is used alongside adversarial loss to precisely detect thin wires. Experimental results obtained on real world scenarios demonstrate the superiority of proposed method over state-of-the-art techniques.
