In the field of photogrammetric engineering, computer vision, and graphics, local shape description is an active research area. A wide variety of local shape descriptors (LSDs) have been designed for different applications, such as shape retrieval, object recognition, and 3D registration. The local reference frame (LRF) is an important component of the LSD. Its repeatability and robustness directly influence the descriptiveness and robustness of the LSD. Several weighting methods have been proposed to improve the repeatability and robustness of the LRF. However, no comprehensive comparison has been implemented to evaluate their performance under different data modalities and nuisances. In this paper, we focus on the comparison of weighting methods by using six datasets with different data modalities and application contexts. We evaluate the repeatability of the LRF under different nuisances, including occlusion, clutter, partial overlap, varying support radii, Gaussian noise, shot noise, point density variation, and keypoint localization error. Through the experiments, the traits, advantages, and disadvantages of weighting methods are summarized.