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Registration aims at merging multiple scans to cover all scenes of a large environment. Thus, it is crucial to many civil infrastructure applications based on three-dimensional (3D) models. However, in many real-world scenarios, it is necessary to align point clouds with low-density or small overlaps. It is difficult to extract stable features and enough features for registration, whether keypoint features or overall posture features, under this condition. Existing methods cannot solve this problem well. This work proposed an end-to-end registration network that can self-adaptively focus on the overlap. The network learned to directly encode posture information from the overlapping area instead of using sparse keypoint correspondences, which makes the network more generalized and efficient. This work also proposed a self-supervised overlapping detector as an extension module to expand the use of this network to align large-scale point clouds of indoor building environments. The proposed detector is compatible with any registration approaches to promote their accuracy and efficiency further. The proposed network was experimentally demonstrated to outperform the state-of-the-art methods in registering sparse and low-overlapping point clouds, with higher robustness to point density and overlap ratio change. The proposed detector can reliably detect the overlapping area and empower the network to accurately align the sparse and low-overlapping point clouds of the large-scale indoor scene, thus simplifying and promoting laser scanning practices in civil infrastructure applications. [ABSTRACT FROM AUTHOR]