  In the realm of point cloud registration, the most prevalent pose evaluation
approaches are statistics-based, identifying the optimal transformation by
maximizing the number of consistent correspondences. However, registration
recall decreases significantly when point clouds exhibit a low overlap rate,
despite efforts in designing feature descriptors and establishing
correspondences. In this paper, we introduce Deep-PE, a lightweight,
learning-based pose evaluator designed to enhance the accuracy of pose
selection, especially in challenging point cloud scenarios with low overlap.
Our network incorporates a Pose-Aware Attention (PAA) module to simulate and
learn the alignment status of point clouds under various candidate poses,
alongside a Pose Confidence Prediction (PCP) module that predicts the
likelihood of successful registration. These two modules facilitate the
learning of both local and global alignment priors. Extensive tests across
multiple benchmarks confirm the effectiveness of Deep-PE. Notably, on 3DLoMatch
with a low overlap rate, Deep-PE significantly outperforms state-of-the-art
methods by at least 8% and 11% in registration recall under handcrafted FPFH
and learning-based FCGF descriptors, respectively. To the best of our
knowledge, this is the first study to utilize deep learning to select the
optimal pose without the explicit need for input correspondences.
