  Local geometric descriptors remain an essential component for 3D rigid data
matching and fusion. The devise of a rotational invariant local geometric
descriptor usually consists of two steps: local reference frame (LRF)
construction and feature representation. Existing evaluation efforts have
mainly been paid on the LRF or the overall descriptor, yet the quantitative
comparison of feature representations remains unexplored. This paper fills this
gap by comprehensively evaluating nine state-of-the-art local geometric feature
representations. Our evaluation is on the ground that ground-truth LRFs are
leveraged such that the ranking of tested feature representations are more
convincing as opposed to existing studies. The experiments are deployed on six
standard datasets with various application scenarios (shape retrieval, point
cloud registration, and object recognition) and data modalities (LiDAR, Kinect,
and Space Time) as well as perturbations including Gaussian noise, shot noise,
data decimation, clutter, occlusion, and limited overlap. The evaluated terms
cover the major concerns for a feature representation, e.g., distinctiveness,
robustness, compactness, and efficiency. The outcomes present interesting
findings that may shed new light on this community and provide complementary
perspectives to existing evaluations on the topic of local geometric feature
description. A summary of evaluated methods regarding their peculiarities is
also presented to guide real-world applications and new descriptor crafting.
