The precision of unsupervised point cloud registration methods is typically
limited by the lack of reliable inlier estimation and self-supervised signal,
especially in partially overlapping scenarios. In this paper, we propose an
effective inlier estimation method for unsupervised point cloud registration by
capturing geometric structure consistency between the source point cloud and
its corresponding reference point cloud copy. Specifically, to obtain a high
quality reference point cloud copy, an One-Nearest Neighborhood (1-NN) point
cloud is generated by input point cloud. This facilitates matching map
construction and allows for integrating dual neighborhood matching scores of
1-NN point cloud and input point cloud to improve matching confidence.
Benefiting from the high quality reference copy, we argue that the neighborhood
graph formed by inlier and its neighborhood should have consistency between
source point cloud and its corresponding reference copy. Based on this
observation, we construct transformation-invariant geometric structure
representations and capture geometric structure consistency to score the inlier
confidence for estimated correspondences between source point cloud and its
reference copy. This strategy can simultaneously provide the reliable
self-supervised signal for model optimization. Finally, we further calculate
transformation estimation by the weighted SVD algorithm with the estimated
correspondences and corresponding inlier confidence. We train the proposed
model in an unsupervised manner, and extensive experiments on synthetic and
real-world datasets illustrate the effectiveness of the proposed method.

我们提出了一种有效的无监督点云配准方法，通过捕捉源点云与其对应的参考点云副本之间的几何结构一致性，实现了对内点的可靠估计和自监督信号，从而提高了匹配的准确度和优化模型完成的鲁棒性。

无监督点云配准中的最近邻指导内点估计

One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point  Cloud Registration

Unsupervised point cloud registration algorithm usually suffers from the
unsatisfied registration precision in the partially overlapping problem due to
the lack of effective inlier evaluation. In this paper, we propose a
neighborhood consensus based reliable inlier evaluation method for robust
unsupervised point cloud registration. It is expected to capture the
discriminative geometric difference between the source neighborhood and the
corresponding pseudo target neighborhood for effective inlier distinction.
Specifically, our model consists of a matching map refinement module and an
inlier evaluation module. In our matching map refinement module, we improve the
point-wise matching map estimation by integrating the matching scores of
neighbors into it. The aggregated neighborhood information potentially
facilitates the discriminative map construction so that high-quality
correspondences can be provided for generating the pseudo target point cloud.
Based on the observation that the outlier has the significant structure-wise
difference between its source neighborhood and corresponding pseudo target
neighborhood while this difference for inlier is small, the inlier evaluation
module exploits this difference to score the inlier confidence for each
estimated correspondence. In particular, we construct an effective graph
representation for capturing this geometric difference between the
neighborhoods. Finally, with the learned correspondences and the corresponding
inlier confidence, we use the weighted SVD algorithm for transformation
estimation. Under the unsupervised setting, we exploit the Huber function based
global alignment loss, the local neighborhood consensus loss, and spatial
consistency loss for model optimization. The experimental results on extensive
datasets demonstrate that our unsupervised point cloud registration method can
yield comparable performance.

该论文提出了一种基于邻域一致性的可靠内点评估方法，以提高无监督点云配准的精度，并利用匹配映射细化模块和内点评估模块配合实现精确配准，最终使用加权 SVD 算法进行转换估计。