G-SHOT: GPU accelerated 3D local descriptor for surface matching

• Of massively parallel algorithms for high density and large scale 3D object local descriptors.
• The exact parallel descriptor with no loss in descriptiveness with a speedup factor of up to 40.70.
• The approximate parallel descriptor with speedup of up to 54 with minor descriptiveness loss.
• Analysis of descriptiveness of both algorithms through recall–precision curves at two noise levels.
• Integration of parallel algorithms into open source point cloud library (PCL).

Signature of histogram of orientations (SHOT) as a novel 3D object local descriptor can achieves a good balance between descriptiveness and robustness in surface matching. However, its computation workload is much higher than the other 3D local descriptors. This paper investigates the development of suitable massively parallel algorithms on the graphics processing unit (GPU) for computation of high density and large scale 3D object local descriptors through two alternative parallel algorithms; one exact, and one approximate. Both algorithms exhibit outstanding speedup performance. The exact parallel descriptor comes at no cost to the descriptiveness, with a speedup factor of up to 40.70, with respect to the serial SHOT on the central processing unit (CPU). The approximate version achieves a corresponding speedup factor of up to 54 with minor degradation in descriptiveness. The proposed algorithms are integrated into point cloud library (PCL), a open source project for image and point cloud.