  We propose a method for detecting structural changes in a city using images
captured from vehicular mounted cameras over traversals at two different times.
We first generate 3D point clouds for each traversal from the images and
approximate GNSS/INS readings using Structure-from-Motion (SfM). A direct
comparison of the two point clouds for change detection is not ideal due to
inaccurate geo-location information and possible drifts in the SfM. To
circumvent this problem, we propose a deep learning-based non-rigid
registration on the point clouds which allows us to compare the point clouds
for structural change detection in the scene. Furthermore, we introduce a dual
thresholding check and post-processing step to enhance the robustness of our
method. We collect two datasets for the evaluation of our approach. Experiments
show that our method is able to detect scene changes effectively, even in the
presence of viewpoint and illumination differences.
