PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised
  RGB-D Point Cloud Registration
Mingzhi Yuan Kexue Fu Zhihao Li Yucong Meng Manning Wang
Point cloud registration is a task to estimate the rigid transformation
between two unaligned scans, which plays an important role in many computer
vision applications. Previous learning-based works commonly focus on supervised
registration, which have limitations in practice. Recently, with the advance of
inexpensive RGB-D sensors, several learning-based works utilize RGB-D data to
achieve unsupervised registration. However, most of existing unsupervised
methods follow a cascaded design or fuse RGB-D data in a unidirectional manner,
which do not fully exploit the complementary information in the RGB-D data. To
leverage the complementary information more effectively, we propose a network
implementing multi-scale bidirectional fusion between RGB images and point
clouds generated from depth images. By bidirectionally fusing visual and
geometric features in multi-scales, more distinctive deep features for
correspondence estimation can be obtained, making our registration more
accurate. Extensive experiments on ScanNet and 3DMatch demonstrate that our
method achieves new state-of-the-art performance. Code will be released at
https://github.com/phdymz/PointMBF