  Constructing a point cloud for a large geographic region, such as a state or
country, can require multiple years of effort. Often several vendors will be
used to acquire LiDAR data, and a single region may be captured by multiple
LiDAR scans. A key challenge is maintaining consistency between these scans,
which includes point density, number of returns, and intensity. Intensity in
particular can be very different between scans, even in areas that are
overlapping. Harmonizing the intensity between scans to remove these
discrepancies is expensive and time consuming. In this paper, we propose a
novel method for point cloud harmonization based on deep neural networks. We
evaluate our method quantitatively and qualitatively using a high quality real
world LiDAR dataset. We compare our method to several baselines, including
standard interpolation methods as well as histogram matching. We show that our
method performs as well as the best baseline in areas with similar intensity
distributions, and outperforms all baselines in areas with different intensity
distributions. Source code is available at
https://github.com/mvrl/lidar-harmonization .
