  Building height estimation is important in many applications such as 3D city
reconstruction, urban planning, and navigation. Recently, a new building height
estimation method using street scene images and 2D maps was proposed. This
method is more scalable than traditional methods that use high-resolution
optical data, LiDAR data, or RADAR data which are expensive to obtain. The
method needs to detect building rooflines and then compute building height via
the pinhole camera model. We observe that this method has limitations in
handling complex street scene images in which buildings overlap with each other
and the rooflines are difficult to locate. We propose CBHE, a building height
estimation algorithm considering both building corners and rooflines. CBHE
first obtains building corner and roofline candidates in street scene images
based on building footprints from 2D maps and the camera parameters. Then, we
use a deep neural network named BuildingNet to classify and filter corner and
roofline candidates. Based on the valid corners and rooflines from BuildingNet,
CBHE computes building height via the pinhole camera model. Experimental
results show that the proposed BuildingNet yields a higher accuracy on building
corner and roofline candidate filtering compared with the state-of-the-art open
set classifiers. Meanwhile, CBHE outperforms the baseline algorithm by over 10%
in building height estimation accuracy.
