Bootstrap Your Own Correspondences
This addresses the scalability limitation of supervised methods for 3D feature learning in point cloud registration, offering a self-supervised alternative that is competitive with top approaches.
The paper tackles the problem of learning geometric features for point cloud registration without ground-truth annotations by proposing BYOC, a self-supervised approach that bootstraps from randomly-initialized CNNs, and it outperforms traditional and learned descriptors while being competitive with supervised state-of-the-art methods on indoor scene datasets.
Geometric feature extraction is a crucial component of point cloud registration pipelines. Recent work has demonstrated how supervised learning can be leveraged to learn better and more compact 3D features. However, those approaches' reliance on ground-truth annotation limits their scalability. We propose BYOC: a self-supervised approach that learns visual and geometric features from RGB-D video without relying on ground-truth pose or correspondence. Our key observation is that randomly-initialized CNNs readily provide us with good correspondences; allowing us to bootstrap the learning of both visual and geometric features. Our approach combines classic ideas from point cloud registration with more recent representation learning approaches. We evaluate our approach on indoor scene datasets and find that our method outperforms traditional and learned descriptors, while being competitive with current state-of-the-art supervised approaches.