In this work, we present a novel method called WSDesc to learn 3D local
descriptors in a weakly supervised manner for robust point cloud registration.
Our work builds upon recent 3D CNN-based descriptor extractors, which leverage
a voxel-based representation to parameterize local geometry of 3D points.
Instead of using a predefined fixed-size local support in voxelization, we
propose to learn the optimal support in a data-driven manner. To this end, we
design a novel differentiable voxelization layer that can back-propagate the
gradient to the support size optimization. To train the extracted descriptors,
we propose a novel registration loss based on the deviation from rigidity of 3D
transformations, and the loss is weakly supervised by the prior knowledge that
the input point clouds have partial overlap, without requiring ground-truth
alignment information. Through extensive experiments, we show that our learned
descriptors yield superior performance on existing geometric registration
benchmarks.

WSDesc 是一种在弱监督下学习 3D 局部描述符的新方法，通过学习最佳支持来优化体素化中的局部支持大小，并采用基于偏离刚性的新型配准损失来训练提取的描述符，其在现有的几何配准基准测试中均表现良好。

弱监督的三维局部描述符学习用于点云配准

WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud  Registration

We present PPF-FoldNet for unsupervised learning of 3D local descriptors on
pure point cloud geometry. Based on the folding-based auto-encoding of well
known point pair features, PPF-FoldNet offers many desirable properties: it
necessitates neither supervision, nor a sensitive local reference frame,
benefits from point-set sparsity, is end-to-end, fast, and can extract powerful
rotation invariant descriptors. Thanks to a novel feature visualization, its
evolution can be monitored to provide interpretable insights. Our extensive
experiments demonstrate that despite having six degree-of-freedom invariance
and lack of training labels, our network achieves state of the art results in
standard benchmark datasets and outperforms its competitors when rotations and
varying point densities are present. PPF-FoldNet achieves $9\%$ higher recall
on standard benchmarks, $23\%$ higher recall when rotations are introduced into
the same datasets and finally, a margin of $>35\%$ is attained when point
density is significantly decreased.

本研究提出了 PPF-FoldNet，它基于基于折叠的自编码器计算点对特征，可以用于学习 3D 本地描述符，具有旋转不变性，并且在标准基准数据集上取得了最新的结果。