We study the problem of extracting accurate correspondences for point cloud
registration. Recent keypoint-free methods have shown great potential through
bypassing the detection of repeatable keypoints which is difficult to do
especially in low-overlap scenarios. They seek correspondences over downsampled
superpoints, which are then propagated to dense points. Superpoints are matched
based on whether their neighboring patches overlap. Such sparse and loose
matching requires contextual features capturing the geometric structure of the
point clouds. We propose Geometric Transformer, or GeoTransformer for short, to
learn geometric feature for robust superpoint matching. It encodes pair-wise
distances and triplet-wise angles, making it invariant to rigid transformation
and robust in low-overlap cases. The simplistic design attains surprisingly
high matching accuracy such that no RANSAC is required in the estimation of
alignment transformation, leading to $100$ times acceleration. Extensive
experiments on rich benchmarks encompassing indoor, outdoor, synthetic,
multiway and non-rigid demonstrate the efficacy of GeoTransformer. Notably, our
method improves the inlier ratio by $18{\sim}31$ percentage points and the
registration recall by over $7$ points on the challenging 3DLoMatch benchmark.
Our code and models are available at
https://github.com/qinzheng93/GeoTransformer.

本论文研究了点云配准中准确提取对应关系的问题，通过绕过重复关键点的检测，利用超点实现了匹配，并提出了一种名为几何变换器（GeoTransformer）的方法来学习几何特征，从而使得在低重叠情况下具有鲁棒性。实验证明该方法在各种测试场景下都表现出了显著的准确性和效率。

GeoTransformer: 快速且鲁棒的点云配准算法与几何变换

GeoTransformer: Fast and Robust Point Cloud Registration with Geometric  Transformer

We study the problem of extracting accurate correspondences for point cloud
registration. Recent keypoint-free methods bypass the detection of repeatable
keypoints which is difficult in low-overlap scenarios, showing great potential
in registration. They seek correspondences over downsampled superpoints, which
are then propagated to dense points. Superpoints are matched based on whether
their neighboring patches overlap. Such sparse and loose matching requires
contextual features capturing the geometric structure of the point clouds. We
propose Geometric Transformer to learn geometric feature for robust superpoint
matching. It encodes pair-wise distances and triplet-wise angles, making it
robust in low-overlap cases and invariant to rigid transformation. The
simplistic design attains surprisingly high matching accuracy such that no
RANSAC is required in the estimation of alignment transformation, leading to
$100$ times acceleration. Our method improves the inlier ratio by $17{\sim}30$
percentage points and the registration recall by over $7$ points on the
challenging 3DLoMatch benchmark. Our code and models are available at
https://github.com/qinzheng93/GeoTransformer.

本研究提出了一种名为几何变换器的方法，通过学习几何特征进行超级点匹配，从而实现点云配准。该方法不需要检测重复的关键点，并在低重叠情况下表现良好，能够提高内点比率和注册召回率。