In this paper, we consider the problem of reconstructing the full layout of an indoor room from a few RGB images. Taking an arbitrary number of images as input, a novel deep Convolutional Neural Network (CNN) architecture is proposed to learn the wall segmentation and features for matching walls across different images in a unified manner. Unobserved areas are then completed by leveraging the information from large-scale datasets. To the best of our knowledge, we are the first to deal with the problem of generic room layouts including the non-cuboid and even non-Manhattan building architectures using RGB images. Additionally, we can produce a holistic layout reconstruction of the indoor room with around 4 images while SfM-based methods usually fail due to the sparsity of input data.

在本文中，我们考虑了从几个RGB图像重建室内空间的完整布局的问题。以任意数量的图像为输入，提出了一种新颖的深度卷积神经网络（CNN）架构，以统一地学习墙分割和特征，以匹配不同图像之间的墙。然后，利用大型数据集的信息完成未观察到的区域。据我们所知，我们是第一个使用RGB图像处理通用房间布局问题的人，包括非立方体甚至非曼哈顿建筑。此外，我们可以使用约4张图像对室内空间进行整体布局重建，而基于SfM的方法通常由于输入数据的稀疏性而失败。