3D building reconstruction from monocular remote sensing images is an
important and challenging research problem that has received increasing
attention in recent years, owing to its low cost of data acquisition and
availability for large-scale applications. However, existing methods rely on
expensive 3D-annotated samples for fully-supervised training, restricting their
application to large-scale cross-city scenarios. In this work, we propose
MLS-BRN, a multi-level supervised building reconstruction network that can
flexibly utilize training samples with different annotation levels to achieve
better reconstruction results in an end-to-end manner. To alleviate the demand
on full 3D supervision, we design two new modules, Pseudo Building Bbox
Calculator and Roof-Offset guided Footprint Extractor, as well as new tasks and
training strategies for different types of samples. Experimental results on
several public and new datasets demonstrate that our proposed MLS-BRN achieves
competitive performance using much fewer 3D-annotated samples, and
significantly improves the footprint extraction and 3D reconstruction
performance compared with current state-of-the-art. The code and datasets of
this work will be released at this https URL

提出了一种适用于大规模交叉城市场景的多级监督建筑重建网络 (MLS-BRN)，旨在通过灵活利用带有不同注释级别的训练样本，在端到端的方式下实现更好的重建结果。实验证明，相较于现有的最先进方法，所提出的 MLS-BRN 在使用更少的三维注释样本的情况下能够达到有竞争力的性能，并显著提高了足迹提取和三维重建的性能。