We present a fast feature-metric point cloud registration framework, which
enforces the optimisation of registration by minimising a feature-metric
projection error without correspondences. The advantage of the feature-metric
projection error is robust to noise, outliers and density difference in
contrast to the geometric projection error. Besides, minimising the
feature-metric projection error does not need to search the correspondences so
that the optimisation speed is fast. The principle behind the proposed method
is that the feature difference is smallest if point clouds are aligned very
well. We train the proposed method in a semi-supervised or unsupervised
approach, which requires limited or no registration label data. Experiments
demonstrate our method obtains higher accuracy and robustness than the
state-of-the-art methods. Besides, experimental results show that the proposed
method can handle significant noise and density difference, and solve both
same-source and cross-source point cloud registration.

提出了一个基于特征度量的点云配准框架，通过最小化特征度量投影误差来实现优化，该方法具有噪声、异常值和密度差异的鲁棒性，并且不需要对应搜索，因此速度很快。实验证明，该方法具有更高的准确性和鲁棒性，并可处理噪声和密度差异，同时能解决同源和异源点云配准问题。