  Multi-view point cloud registration is fundamental in 3D reconstruction.
Since there are close connections between point clouds captured from different
viewpoints, registration performance can be enhanced if these connections be
harnessed properly. Therefore, this paper models the registration problem as
multi-task optimization, and proposes a novel bi-channel knowledge sharing
mechanism for effective and efficient problem solving. The modeling of
multi-view point cloud registration as multi-task optimization are twofold. By
simultaneously considering the local accuracy of two point clouds as well as
the global consistency posed by all the point clouds involved, a fitness
function with an adaptive threshold is derived. Also a framework of the
co-evolutionary search process is defined for the concurrent optimization of
multiple fitness functions belonging to related tasks. To enhance solution
quality and convergence speed, the proposed bi-channel knowledge sharing
mechanism plays its role. The intra-task knowledge sharing introduces aiding
tasks that are much simpler to solve, and useful information is shared across
aiding tasks and the original tasks, accelerating the search process. The
inter-task knowledge sharing explores commonalities buried among the original
tasks, aiming to prevent tasks from getting stuck to local optima.
Comprehensive experiments conducted on model object as well as scene point
clouds show the efficacy of the proposed method.
