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 3DMatchnoise 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.

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This study proposes a distinctive and robust spatial and geometric histograms (SGHs) feature descriptor for three‐dimensional (3D) local surface description. The authors also introduce a new local reference frame for the generation of their SGH descriptor. To fully describe a local surface, the SGH descriptor considers both spatial distribution and geometrical characteristics in its underlying support region. To encode neighbourhood information, the SGH descriptor is constructed using histogram statistics with spatial partition and interpolation strategies. The performance of the SGH descriptor was rigorously tested on six public datasets for applications of both 3D object recognition and registration. Compared to eight state‐of‐the‐art descriptors, experimental results show that SGH achieves the best performance on noise‐free data. It also produces the best results even under different nuisances. The promising descriptiveness and robustness of their SGH descriptor have been fully demonstrated.

Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this paper, we introduce a new, yet conceptually simple, neural architecture, termed SpinNet, to extract local features which are rotationally invariant whilst sufficiently informative to enable accurate registration. A Spatial Point Transformer is first introduced to map the input local surface into a carefully designed cylindrical space, enabling end-to-end optimization with SO(2) equivariant representation. A Neural Feature Extractor which leverages the powerful point-based and 3D cylindrical convolutional neural layers is then utilized to derive a compact and representative descriptor for matching. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-theart techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities. The code is available at https: //github.com/QingyongHu/SpinNet.