Abstract
Recent advance in high-accuracy sensors has made point cloud become the main data format to characterize the three-dimensional world. Since the sensor can only scan and capture the 3D data within a limited field of view, an alignment algorithm is needed to generate the complete 3D scene. Point cloud registration is the solution for alignment problem that aims to estimate the transformation matrix between two frames of different point cloud sets. In this paper, we propose a neural network called OLFF-Net to achieve robust registration of 3D point clouds based on overlapped local feature fusion, which focuses on extracting rotational-invariant local features while providing enough information to achieve accurate alignment. Extensive experiments on representative datasets indicate that the framework can largely outperform competing methods with an average improvement of 16.82% in the metrics over the compared methods. More importantly, it shows significant generalization capability and can be widely applied to point cloud data with multiple complex structures.
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