  Registration of point clouds collected from a pair of distant vehicles
provides a comprehensive and accurate 3D view of the driving scenario, which is
vital for driving safety related applications, yet existing literature suffers
from the expensive pose label acquisition and the deficiency to generalize to
new data distributions. In this paper, we propose EYOC, an unsupervised distant
point cloud registration method that adapts to new point cloud distributions on
the fly, requiring no global pose labels. The core idea of EYOC is to train a
feature extractor in a progressive fashion, where in each round, the feature
extractor, trained with near point cloud pairs, can label slightly farther
point cloud pairs, enabling self-supervision on such far point cloud pairs.
This process continues until the derived extractor can be used to register
distant point clouds. Particularly, to enable high-fidelity correspondence
label generation, we devise an effective spatial filtering scheme to select the
most representative correspondences to register a point cloud pair, and then
utilize the aligned point clouds to discover more correct correspondences.
Experiments show that EYOC can achieve comparable performance with
state-of-the-art supervised methods at a lower training cost. Moreover, it
outwits supervised methods regarding generalization performance on new data
distributions.
