Abstract
PointNet has revolutionized how we think about representing point clouds. For classification and segmentation
tasks, the approach and its subsequent extensions are stateof-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.