TY - JOUR
T1 - Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching
AU - Yang, Jiaqi
AU - Quan, Siwen
AU - Wang, Peng
AU - Zhang, Yanning
N1 - Publisher Copyright:
© 1992-2012 IEEE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - 3D matching
KW - 3D point cloud
KW - Performance evaluation
KW - feature representation
KW - local descriptors
UR - http://www.scopus.com/inward/record.url?scp=85078542822&partnerID=8YFLogxK
U2 - 10.1109/TIP.2019.2959236
DO - 10.1109/TIP.2019.2959236
M3 - 文章
AN - SCOPUS:85078542822
SN - 1057-7149
VL - 29
SP - 2522
EP - 2535
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
M1 - 8935529
ER -