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.

在点云配准领域，本文介绍了一种轻量级的基于学习的姿态评估器（Deep-PE），用于增强姿态选择的准确性，特别是在低重叠的挑战性点云场景中。该网络结合了姿态感知注意力模块和姿态置信度预测模块，通过学习不同候选姿态下点云的对齐状态来提高注册的成功率。在多个基准测试中，Deep-PE 在低重叠率的情况下，相对于基于手工 FPFH 和基于学习的 FCGF 描述符的最先进方法，注册召回率分别提高了至少 8% 和 11%。据我们所知，这是第一项利用深度学习在没有显式输入对应关系的情况下选择最优姿态的研究。