室内布局估计是计算机视觉领域的研究热点之一,在三维重建、机器人导航和虚拟现实等方面具有广泛的应用。目前室内布局估计的解决方案存在实时性较差、计算量较大等问题。针对这些问题,本文提出了一种基于多任务监督的轻量卷积网络,该网络模型基于编码器-解码器结构,使用室内边缘热图与平面语义分割实现多任务监督学习。此外本文对卷积模块进行了改进,使用1×1卷积替换了1×3、3×1卷积,在保证模型精度的情况下提升了网络的实时性。在公共数据集LSUN上进行实验,结果表明,本文方法具有良好的实时性和准确性。

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.