Abstract. Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space.
The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development
of robust matching strategies on the other hand. In this work, we first
propose a multi-view local descriptor, which is learned from the images
of multiple views, for the description of 3D keypoints. Then, we develop
a robust matching approach, aiming at rejecting outlier matches based
on the efficient inference via belief propagation on the defined graphical
model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior
performance has been verified by the intensive comparisons against a
variety of descriptors and matching methods