PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
This addresses the problem of robust and efficient point cloud registration for computer vision and robotics applications, representing an incremental advancement by combining existing techniques in a novel way.
The paper tackled point cloud registration by adapting the Lucas & Kanade algorithm to work with PointNet as a learnable imaging function, resulting in a method that generalizes across shape categories and is computationally efficient, though specific performance numbers are not provided in the abstract.
PointNet has revolutionized how we think about representing point clouds. For classification and segmentation tasks, the approach and its subsequent extensions are state-of-the-art. To date, the successful application of PointNet to point cloud registration has remained elusive. In this paper we argue that PointNet itself can be thought of as a learnable "imaging" function. As a consequence, classical vision algorithms for image alignment can be applied on the problem - namely the Lucas & Kanade (LK) algorithm. Our central innovations stem from: (i) how to modify the LK algorithm to accommodate the PointNet imaging function, and (ii) unrolling PointNet and the LK algorithm into a single trainable recurrent deep neural network. We describe the architecture, and compare its performance against state-of-the-art in common registration scenarios. The architecture offers some remarkable properties including: generalization across shape categories and computational efficiency - opening up new paths of exploration for the application of deep learning to point cloud registration. Code and videos are available at https://github.com/hmgoforth/PointNetLK.