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
this https URL

利用 RGB 图像和深度图像生成的点云之间的多尺度双向融合实现了更准确的对应估计，从而使得我们的点云配准方法在 ScanNet 和 3DMatch 数据集上达到了最新的性能水平。