  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 https://github.com/opendatalab/MLS-BRN.git.
