  Point cloud registration aims at estimating the geometric transformation
between two point cloud scans, in which point-wise correspondence estimation is
the key to its success. In addition to previous methods that seek
correspondences by hand-crafted or learnt geometric features, recent point
cloud registration methods have tried to apply RGB-D data to achieve more
accurate correspondence. However, it is not trivial to effectively fuse the
geometric and visual information from these two distinctive modalities,
especially for the registration problem. In this work, we propose a new
Geometry-Aware Visual Feature Extractor (GAVE) that employs multi-scale local
linear transformation to progressively fuse these two modalities, where the
geometric features from the depth data act as the geometry-dependent
convolution kernels to transform the visual features from the RGB data. The
resultant visual-geometric features are in canonical feature spaces with
alleviated visual dissimilarity caused by geometric changes, by which more
reliable correspondence can be achieved. The proposed GAVE module can be
readily plugged into recent RGB-D point cloud registration framework. Extensive
experiments on 3D Match and ScanNet demonstrate that our method outperforms the
state-of-the-art point cloud registration methods even without correspondence
or pose supervision. The code is available at: https://github.com/514DNA/LLT.
