Enhancing Monocular Height Estimation from Aerial Images with
  Street-view Images
Xiaomou Hou Wanshui Gan Naoto Yokoya
Accurate height estimation from monocular aerial imagery presents a
significant challenge due to its inherently ill-posed nature. This limitation
is rooted in the absence of adequate geometric constraints available to the
model when training with monocular imagery. Without additional geometric
information to supplement the monocular image data, the model's ability to
provide reliable estimations is compromised.
  In this paper, we propose a method that enhances monocular height estimation
by incorporating street-view images. Our insight is that street-view images
provide a distinct viewing perspective and rich structural details of the
scene, serving as geometric constraints to enhance the performance of monocular
height estimation. Specifically, we aim to optimize an implicit 3D scene
representation, density field, with geometry constraints from street-view
images, thereby improving the accuracy and robustness of height estimation. Our
experimental results demonstrate the effectiveness of our proposed method,
outperforming the baseline and offering significant improvements in terms of
accuracy and structural consistency.