Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching

Local geometric descriptors act as an essential component for 3D rigid data matching. A rotational invariant local geometric descriptor usually consists of two components: local reference frame (LRF) and feature representation. However, existing evaluation efforts have mainly been paid on the LRF or the overall descriptor and the quantitative comparison of feature representations remains unexplored. This paper fills the gap by comprehensively evaluating nine state-of-the-art local geometric feature representations. In particular, our evaluation assesses feature representations based on ground-truth LRFs such that the ranking of tested methods is more convincing as compared with 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 finally presented to guide real-world applications and new descriptor crafting.