Learning-based outlier (mismatched correspondence) rejection for robust 3D
registration generally formulates the outlier removal as an inlier/outlier
classification problem. The core for this to be successful is to learn the
discriminative inlier/outlier feature representations. In this paper, we
develop a novel variational non-local network-based outlier rejection framework
for robust alignment. By reformulating the non-local feature learning with
variational Bayesian inference, the Bayesian-driven long-range dependencies can
be modeled to aggregate discriminative geometric context information for
inlier/outlier distinction. Specifically, to achieve such Bayesian-driven
contextual dependencies, each query/key/value component in our non-local
network predicts a prior feature distribution and a posterior one. Embedded
with the inlier/outlier label, the posterior feature distribution is
label-dependent and discriminative. Thus, pushing the prior to be close to the
discriminative posterior in the training step enables the features sampled from
this prior at test time to model high-quality long-range dependencies. Notably,
to achieve effective posterior feature guidance, a specific probabilistic
graphical model is designed over our non-local model, which lets us derive a
variational low bound as our optimization objective for model training.
Finally, we propose a voting-based inlier searching strategy to cluster the
high-quality hypothetical inliers for transformation estimation. Extensive
experiments on 3DMatch, 3DLoMatch, and KITTI datasets verify the effectiveness
of our method.

本文提出了一种基于变分非局部网络的离群值识别框架，使用贝叶斯驱动的长程依赖关系来聚集区分几何背景信息进行内点 / 外点的区分，通过投票的方式搜索高质量内点集合进行变换估计，实验证明该框架在 3DMatch、3DLoMatch 和 KITTI 数据集上表现良好。