TY - JOUR
T1 - Compatibility-Guided Sampling Consensus for 3-D Point Cloud Registration
AU - Quan, Siwen
AU - Yang, Jiaqi
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - This article presents an efficient and robust estimator called compatibility-guided sampling consensus (CG-SAC) to achieve accurate 3-D point cloud registration. For correspondence-based registration methods, the random sample consensus (RANSAC) is served as a de facto solution for rigid transformation estimation from a number of feature correspondences. Unfortunately, RANSAC still suffers from two major limitations. First, it generates a hypothesis with at least three samples and desires a very large number of iterations to attain reasonable results, making it relatively time consuming. Second, the randomness during sampling can result in inaccurate results as it is highly potential to miss the optimal hypothesis. To solve these problems, we propose a compatibility-guided sampling strategy to eliminate randomness during sampling. In particular, only two correspondences are required by our method for hypothesis generation. We then rank correspondence pairs according to their compatibility scores because compatible correspondences are more likely to be correct and can yield more reasonable hypotheses. In addition, we propose a new geometric constraint named the distance between salient points (DSP) to measure the compatibility of two correspondences. Experiments on a set of real-world point cloud data with different application contexts and data modalities confirm the effectiveness of the proposed method. Comparison with several state-of-the-art estimators demonstrates the overall superiority of our CG-SAC estimator with regards to precision and time efficiency.
AB - This article presents an efficient and robust estimator called compatibility-guided sampling consensus (CG-SAC) to achieve accurate 3-D point cloud registration. For correspondence-based registration methods, the random sample consensus (RANSAC) is served as a de facto solution for rigid transformation estimation from a number of feature correspondences. Unfortunately, RANSAC still suffers from two major limitations. First, it generates a hypothesis with at least three samples and desires a very large number of iterations to attain reasonable results, making it relatively time consuming. Second, the randomness during sampling can result in inaccurate results as it is highly potential to miss the optimal hypothesis. To solve these problems, we propose a compatibility-guided sampling strategy to eliminate randomness during sampling. In particular, only two correspondences are required by our method for hypothesis generation. We then rank correspondence pairs according to their compatibility scores because compatible correspondences are more likely to be correct and can yield more reasonable hypotheses. In addition, we propose a new geometric constraint named the distance between salient points (DSP) to measure the compatibility of two correspondences. Experiments on a set of real-world point cloud data with different application contexts and data modalities confirm the effectiveness of the proposed method. Comparison with several state-of-the-art estimators demonstrates the overall superiority of our CG-SAC estimator with regards to precision and time efficiency.
KW - Feature correspondences
KW - geometric compatibility
KW - geometric constraint
KW - point cloud registration
KW - transformation estimation
UR - http://www.scopus.com/inward/record.url?scp=85092414738&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2020.2982221
DO - 10.1109/TGRS.2020.2982221
M3 - 文章
AN - SCOPUS:85092414738
SN - 0196-2892
VL - 58
SP - 7380
EP - 7392
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 10
M1 - 9052691
ER -