SphereNet：学习用于点云配准的抗噪且具泛化性的描述子
SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration
AI总结：
针对现有点云配准描述子抗噪性差、泛化能力弱的问题，提出SphereNet，通过球形体素编码、球面插值及带完整性填充的球面卷积网络提取描述子，在噪声场景和多个基准上取得最优性能与泛化性。
摘要：
点云配准旨在估计一种变换，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，用于学习适用于点云配准的抗噪且对未见过数据具泛化性的描述子。在我们的方法中，首先，球体生成器基于球形体素构建几何域以编码初始特征。随后，引入球体的球面插值实现对噪声的鲁棒性。最后，一种带有球面完整性填充的新型球面卷积神经网络完成描述子提取，该网络减少了特征损失并能充分捕捉几何特征。为评估我们的方法，我们构建了一个含强噪声的新基准3DMatch-noise。我们在室内和室外数据集上开展了大量实验。在高强度噪声下，SphereNet在3DMatch-noise上的特征匹配召回率提升了25个百分点以上。此外，它在3DMatch和3DLoMatch基准上分别取得了93.5%和75.6%的配准召回率，刷新了当前最优性能，且在未见过的数据集上具备最佳泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
