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.

使用八叉树结构从已标注点云向未标注点云进行注释传递，通过分析点云间的变化，该方法可以有效地传递注释并解决变化问题，对于自动在两个不同的代表同一实物的点云之间进行标签传递的方法的开发是该项目的主要贡献，该方法对于数据驱动的深度学习算法具有非常重要的意义，因为它还能够实现不确定性传递学习的确定性标签传递。