Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image
Abstract
Single-image room layout reconstruction aims to reconstruct the enclosed 3D structure of a room from a single image. Most previous work relies on the cuboid-shape prior. This paper considers a more general indoor assumption, i.e., the room layout consists of a single ceiling, a single floor, and several vertical walls. To this end, we first employ Convolutional Neural Networks to detect planes and vertical lines between adjacent walls. Meanwhile, estimating the 3D parameters for each plane. Then, a simple yet effective geometric reasoning method is adopted to achieve room layout reconstruction. Furthermore, we optimize the 3D plane parameters to reconstruct a geometrically consistent room layout between planes and lines. The experimental results on public datasets validate the effectiveness and efficiency of our method.
Cite
@article{arxiv.2104.07986,
title = {Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image},
author = {Cheng Yang and Jia Zheng and Xili Dai and Rui Tang and Yi Ma and Xiaojun Yuan},
journal= {arXiv preprint arXiv:2104.07986},
year = {2021}
}
Comments
To appear in WACV 2022. The first two author contribute equally. Code is available at https://github.com/Cyang0515/NonCuboidRoom