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Indoor scene understanding via RGB-D image segmentation employing depth-based CNN and CRFs

机译：通过使用基于深度的CNN和CRF的RGB-D图像分割的室内场景理解

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摘要

With the availability of low-cost depth-visual sensing devices, such as Microsoft Kinect, we are experiencing a growing interest in indoor environment understanding, at the core of which is semantic segmentation in RGB-D image. The latest research shows that the convolutional neural network (CNN) still dominates the image semantic segmentation field. However, down-sampling operated during the training process of CNNs leads to unclear segmentation boundaries and poor classification accuracy. To address this problem, in this paper, we propose a novel end-to-end deep architecture, termed FuseCRFNet, which seamlessly incorporates a fully-connected Conditional Random Fields (CRFs) model into a depth-based CNN framework. The proposed segmentation method uses the properties of pixel-to-pixel relationships to increase the accuracy of image semantic segmentation. More importantly, we formulate the CRF as one of the layers in FuseCRFNet to refine the coarse segmentation in the forward propagation, in meanwhile, it passes back the errors to facilitate the training. The performance of our FuseCRFNet is evaluated by experimenting with SUN RGB-D dataset, and the results show that the proposed algorithm is superior to existing semantic segmentation algorithms with an improvement in accuracy of at least 2%, further verifying the effectiveness of the algorithm.
机译：随着低成本深度视觉传感设备的可用性，例如Microsoft Kinect，我们在室内环境中遇到了日益增长的兴趣，在RGB-D图像中是语义分割的核心。最新研究表明，卷积神经网络（CNN）仍然占据了图像语义分割领域。然而，在CNNS的训练过程中运行的下式采样导致分割边界不清楚，分类准确性差。为了解决这个问题，在本文中，我们提出了一种新的端到端深度架构，被称为FUSECNETNET，它将完全连接的条件随机字段（CRFS）模型无缝地融入基于深度的CNN框架。所提出的分割方法使用像素到像素关系的属性来提高图像语义分割的准确性。更重要的是，我们将CRF制定为FusECRFNET中的一个层，以改进前向传播中的粗分段，同时它通过误差以方便培训。通过尝试Sun RGB-D数据集来评估我们的FUSECNETNET的性能，结果表明，该算法优于现有的语义分段算法，精度提高至少2％，进一步验证算法的有效性。

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  • 来源
    《Multimedia Tools and Applications》 |2020年第48期|35475-35489|共15页
  • 作者

    Wei Li; Junhua Gu; Yongfeng Dong; Yao Dong; Jungong Han;

  • 作者单位

    School of Electrical Engineering Hebei University of Technology Tianjin 300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province. Hebei University of Technology Tianjin China;

    School of Artificial Intelligence Hebei University of Technology Tianjin 300401 China Key Laboratory of Big Data Computing Hebei Tianjin China;

    School of Artificial Intelligence Hebei University of Technology Tianjin 300401 China Key Laboratory of Big Data Computing Hebei Tianjin China;

    School of Artificial Intelligence Hebei University of Technology Tianjin 300401 China Key Laboratory of Big Data Computing Hebei Tianjin China;

    School of Computing and Communications Lancaster University Lancaster UK;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Sematic segmentation; CNNs; RGB-D; Fully-connected conditional random field;

    机译：语义细分;CNNS;RGB-D;完全连接的条件随机字段;

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