Recent years have seen flourishing research on both semi-supervised learning
and 3D room layout reconstruction. In this work, we explore the intersection of
these two fields to advance the research objective of enabling more accurate 3D
indoor scene modeling with less labeled data. We propose the first approach to
learn representations of room corners and boundaries by using a combination of
labeled and unlabeled data for improved layout estimation in a 360-degree
panoramic scene. Through extensive comparative experiments, we demonstrate that
our approach can advance layout estimation of complex indoor scenes using as
few as 20 labeled examples. When coupled with a layout predictor pre-trained on
synthetic data, our semi-supervised method matches the fully supervised
counterpart using only 12% of the labels. Our work takes an important first
step towards robust semi-supervised layout estimation that can enable many
applications in 3D perception with limited labeled data.

该研究探索了半监督学习和三维室内布局重建的交叉领域，提出了使用标记和未标记数据学习房间角和边界表示的方法，利用 360 度全景场景实现了改进的室内布局估计。实验结果表明，该方法同样精准，仅需使用 12％的标记数据。这项工作是实现使用有限标记数据进行三维感知的强大半监督布局估计的重要第一步。