Point cloud registration is to estimate a transformation to align point clouds collected in different perspectives. In learning-based point cloud registration, a robust descriptor is vital for high-accuracy registration. However, most methods are susceptible to noise and have poor generalization ability on unseen datasets. Motivated by this, we introduce SphereNet to learn a noise-robust and unseen-general descriptor for point cloud registration. In our method, first, the spheroid generator builds a geometric domain based on spherical voxelization to encode initial features. Then, the spherical interpolation of the sphere is introduced to realize robustness against noise. Finally, a new spherical convolutional neural network with spherical integrity padding completes the extraction of descriptors, which reduces the loss of features and fully captures the geometric features. To evaluate our methods, a new benchmark 3DMatch-noise with strong noise is introduced. Extensive experiments are carried out on both indoor and outdoor datasets. Under high-intensity noise, SphereNet increases the feature matching recall by more than 25 percentage points on 3DMatch-noise. In addition, it sets a new state-of-the-art performance for the 3DMatch and 3DLoMatch benchmarks with 93.5\% and 75.6\% registration recall and also has the best generalization ability on unseen datasets.

翻译：点云配准旨在估计变换以对齐从不同视角采集的点云。在基于学习的点云配准中，鲁棒的描述子对实现高精度配准至关重要。然而，大多数方法易受噪声影响，且在未见过的数据集上泛化能力较差。受此启发，我们提出SphereNet，以学习一种对噪声鲁棒且能泛化到未知数据的点云配准描述子。我们的方法中，首先，球体生成器基于球形体素化构建几何域以编码初始特征；其次，引入球面的球面插值以实现对噪声的鲁棒性；最后，一种具有球面完整性填充的新型球面卷积神经网络完成描述子的提取，从而减少特征损失并充分捕获几何特征。为评估方法，我们引入了带有强噪声的新基准3DMatch-noise。在室内和室外数据集上进行了广泛实验。在高强度噪声下，SphereNet在3DMatch-noise上将特征匹配召回率提升了超过25个百分点。此外，它在3DMatch和3DLoMatch基准上分别达到了93.5%和75.6%的配准召回率，创下新纪录，并在未见数据集上展现出最佳的泛化能力。