Globally Optimal Registration of Dense Terrestrial Laser Scans from Coarse Sampling

3D point cloud registration is paramount for mapping complex environments. However, aligning overlapping clouds remains computationally demanding due to the increasing size and density of data from modern Terrestrial Laser Scanners. In this paper, we propose a lightweight, memory-efficient registration approach that operates directly on a coarse level of detail extracted from octree structures. Rather than relying on keypoints, we construct full coarse representations and associate each point with a descriptor to form global feature maps. We evaluate several point distribution strategies for building these representations and show that our approach significantly reduces processing time and memory usage while maintaining or improving registration accuracy.