Embrace descriptors that use point pairs feature.

As technology evolves, the cost of 3D scanners is falling, which makes 3D computer vision for industrial applications increasingly popular. More and more researchers have started to study 3D computer vision. Point cloud feature descriptors are a fundamental task in 3D computer vision, and descriptors that use spatial features tend to perform better than those without them. Point cloud descriptors can generally be divided into local reference frames-based (LRF-based) and local reference frames-free (LRF-free). The former uses LRFs to provide spatial features to the descriptors, while the latter uses point pair features to provide spatial features. However, the performance of those LRF-based descriptors is more affected by local reference frames (LRFs), and the descriptors with spatial information LRF-free tend to be more computationally intensive because of its point pair combination strategy. Therefore, we propose a strategy named Multi-scale Point Pair Combination Strategy (MSPPCS) that reduces the computation of point pair-based feature descriptors by nearly 70 % while ensuring that the performance of the descriptor is almost unaffected. We also propose a new descriptor, Spatial Feature Point Pair Histograms (SFPPH), which has excellent performance and robustness due to the diverse spatial features used. We critically evaluate the performance of our descriptor on the Bologna dataset, Kinect dataset, and UWA dataset. The experimental results show that our descriptor is the most robust and performing point cloud feature descriptor. [ABSTRACT FROM AUTHOR]