TY - GEN
T1 - Dual Transformation and Manifold Distances Voting for Outlier Rejection in Point Cloud Registration
AU - Efraim, Amit
AU - Francos, Joseph M.
N1 - Funding Information:
This research is supported by NSF-BSF Computing and Communication Foundations (CCF) grants, CCF-2016667, CCF-1712788 and BSF-2016667.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - We present a novel outlier rejection scheme for point cloud registration using SE(3) voting on local transformation estimates with a dual consensus constraint. Point cloud registration is commonly performed by matching key-points in both point clouds and estimating the transformation parameters from these matches. In the presented method, each putative matching pair of points is equipped with a local transformation estimate using the Rigid Transformation Universal Manifold Embedding. Putative matching pairs with similar local estimates are then clustered together and the global transformation between point clouds is estimated for each cluster. Finally, the cluster with the majority of the votes such that the average of local transformations agrees with its associated global transformation is selected for completing the registration. This approach successfully deals with up to 99.5% outliers where state of the art fails.
AB - We present a novel outlier rejection scheme for point cloud registration using SE(3) voting on local transformation estimates with a dual consensus constraint. Point cloud registration is commonly performed by matching key-points in both point clouds and estimating the transformation parameters from these matches. In the presented method, each putative matching pair of points is equipped with a local transformation estimate using the Rigid Transformation Universal Manifold Embedding. Putative matching pairs with similar local estimates are then clustered together and the global transformation between point clouds is estimated for each cluster. Finally, the cluster with the majority of the votes such that the average of local transformations agrees with its associated global transformation is selected for completing the registration. This approach successfully deals with up to 99.5% outliers where state of the art fails.
UR - http://www.scopus.com/inward/record.url?scp=85123048848&partnerID=8YFLogxK
U2 - 10.1109/ICCVW54120.2021.00467
DO - 10.1109/ICCVW54120.2021.00467
M3 - Conference contribution
AN - SCOPUS:85123048848
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 4187
EP - 4195
BT - Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
PB - Institute of Electrical and Electronics Engineers
T2 - 18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Y2 - 11 October 2021 through 17 October 2021
ER -