  Identifying changes in a pair of 3D aerial LiDAR point clouds, obtained
during two distinct time periods over the same geographic region presents a
significant challenge due to the disparities in spatial coverage and the
presence of noise in the acquisition system. The most commonly used approaches
to detecting changes in point clouds are based on supervised methods which
necessitate extensive labelled data often unavailable in real-world
applications. To address these issues, we propose an unsupervised approach that
comprises two components: Implicit Neural Representation (INR) for continuous
shape reconstruction and a Gaussian Mixture Model for categorising changes. INR
offers a grid-agnostic representation for encoding bi-temporal point clouds,
with unmatched spatial support that can be regularised to enhance
high-frequency details and reduce noise. The reconstructions at each timestamp
are compared at arbitrary spatial scales, leading to a significant increase in
detection capabilities. We apply our method to a benchmark dataset comprising
simulated LiDAR point clouds for urban sprawling. This dataset encompasses
diverse challenging scenarios, varying in resolutions, input modalities and
noise levels. This enables a comprehensive multi-scenario evaluation, comparing
our method with the current state-of-the-art approach. We outperform the
previous methods by a margin of 10% in the intersection over union metric. In
addition, we put our techniques to practical use by applying them in a
real-world scenario to identify instances of illicit excavation of
archaeological sites and validate our results by comparing them with findings
from field experts.
