SphereNet: Learning a Noise-Robust and General Descriptor for Point
  Cloud Registration
Guiyu Zhao Zhentao Guo Xin Wang Hongbin Ma
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.