Laser & Optoelectronics Progress, Volume. 58, Issue 14, 1410023(2021)
Real-Time Indoor Layout Estimation Method Based on Multi-Task Supervised Learning
Indoor layout estimation is one of the important research topics in the field of computer vision, and it is widely used in three-dimensional reconstruction, robot navigation, and virtual reality. The current indoor layout estimation solutions have problems such as poor real-time performance and large calculations. To deal with these problems, this paper proposes a lightweight convolutional network based on multi-task supervision. The network model is based on the encoder-decoder structure and uses indoor edge heatmaps and planar semantic segmentation to achieve multi-task supervised learning. In addition, this paper modifies the convolution module, replaced 1×3 and 3×1 convolution with 1×1 convolution, which improves the real-time performance of the network while ensuring the accuracy of the model. The experimental results conducted on the public dataset LSUN show that the proposed method has good real-time performance and accuracy.
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Rongze Huang, Qinghao Meng, Yinbo Liu. Real-Time Indoor Layout Estimation Method Based on Multi-Task Supervised Learning[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1410023
Category: Image Processing
Received: Aug. 28, 2020
Accepted: Sep. 30, 2020
Published Online: Jun. 30, 2021
The Author Email: Liu Yinbo (liuyinbo@tju.edu.cn)