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DeepFacade: A Deep Learning Approach to Facade Parsing

机译:Deepfacade:一个深刻的学习方法,即面板解析

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摘要

The parsing of building facades is a key component to the problem of 3D street scenes reconstruction, which is long desired in computer vision. In this paper, we propose a deep learning based method for segmenting a facade into semantic categories. Man-made structures often present the characteristic of symmetry. Based on this observation, we propose a symmetric regularizer for training the neural network. Our proposed method can make use of both the power of deep neural networks and the structure of man-made architectures. We also propose a method to refine the segmentation results using bounding boxes generated by the Region Proposal Network. We test our method by training a FCN-8s network with the novel loss function. Experimental results show that our method has outperformed previous state-of-the-art methods significantly on both the ECP dataset and the eTRIMS dataset. As far as we know, we are the first to employ end-to-end deepconvolutional neural network on full image scale in the task of building facades parsing.
机译:建筑物外观的解析是3D街场景重建问题的关键组成部分,这在计算机视觉中需要很长时间。在本文中,我们提出了一种基于深度学习的方法,用于将立面分割为语义类别。人造结构通常呈现对称性的特征。基于该观察,我们提出了一种对称规范器来训练神经网络。我们所提出的方法可以利用深神经网络的力量和人造架构的结构。我们还提出了一种使用该区域提议网络生成的边界框来改进分段结果的方法。我们通过使用新型损耗功能培训FCN-8S网络来测试我们的方法。实验结果表明,我们的方法在ECP数据集和ETRIMS数据集中显着显着优于先前的最先进的方法。据我们所知,我们是第一个在建筑外墙解析的任务中采用完整图像规模的端到端深度神经网络。

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