TY - GEN
T1 - 3D Matched Manifold Detection for Optimizing Point Cloud Registration
AU - Efraim, Amit
AU - Francos, Joseph M.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Point cloud registration is usually performed by matching key points to obtain an approximate global alignment, followed by a local optimization algorithm such as the iterative closest point (I CP) and its variants, to refine the initial estimate. These refinement algorithms, however, converge in many cases to a false local extremum. We propose a new matched manifold detection approach over the group of rigid 3-D transformations, by employing a novel correlation operator between functions defined on sparsely and non-uniformly sampled point clouds. Correlation between point clouds is evaluated using a method-ology inspired by the definition of the Kernel Point Convolution (KPConv), but instead of performing convolution with a kernel, the inner-product of feature vectors evaluated on the points in the two point clouds are aggregated. The proposed approach is shown to outperform state of the art local registration methods in terms of accuracy on challenging data sets.
AB - Point cloud registration is usually performed by matching key points to obtain an approximate global alignment, followed by a local optimization algorithm such as the iterative closest point (I CP) and its variants, to refine the initial estimate. These refinement algorithms, however, converge in many cases to a false local extremum. We propose a new matched manifold detection approach over the group of rigid 3-D transformations, by employing a novel correlation operator between functions defined on sparsely and non-uniformly sampled point clouds. Correlation between point clouds is evaluated using a method-ology inspired by the definition of the Kernel Point Convolution (KPConv), but instead of performing convolution with a kernel, the inner-product of feature vectors evaluated on the points in the two point clouds are aggregated. The proposed approach is shown to outperform state of the art local registration methods in terms of accuracy on challenging data sets.
KW - Manifold optimization
KW - Matched manifold detection
KW - Point clouds
KW - Registration
KW - Rigid transformation
UR - http://www.scopus.com/inward/record.url?scp=85146416385&partnerID=8YFLogxK
U2 - 10.1109/ICECCME55909.2022.9988221
DO - 10.1109/ICECCME55909.2022.9988221
M3 - Conference contribution
AN - SCOPUS:85146416385
T3 - International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022
BT - International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022
PB - Institute of Electrical and Electronics Engineers
T2 - 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022
Y2 - 16 November 2022 through 18 November 2022
ER -