  Point clouds and high-resolution 3D data have become increasingly important
in various fields, including surveying, construction, and virtual reality.
However, simply having this data is not enough; to extract useful information,
semantic labeling is crucial. In this context, we propose a method to transfer
annotations from a labeled to an unlabeled point cloud using an octree
structure. The structure also analyses changes between the point clouds. Our
experiments confirm that our method effectively transfers annotations while
addressing changes. The primary contribution of this project is the development
of the method for automatic label transfer between two different point clouds
that represent the same real-world object. The proposed method can be of great
importance for data-driven deep learning algorithms as it can also allow
circumventing stochastic transfer learning by deterministic label transfer
between datasets depicting the same objects.
