Efficient and Robust Registration on the 3D Special Euclidean Group

Uttaran Bhattacharya, Venu Madhav Govindu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 5885-5894

We present a robust, fast and accurate method for registration of 3D scans. Using correspondences, our method optimizes a robust cost function on the intrinsic representation of rigid motions, i.e., the Special Euclidean group SE(3). We exploit the geometric properties of Lie groups as well as the robustness afforded by an iteratively reweighted least squares optimization. We also generalize our approach to a joint multiview method that simultaneously solves for the registration of a set of scans. Our approach significantly outperforms the state-of-the-art robust 3D registration method based on a line process in terms of both speed and accuracy. We show that this line process method is a special case of our principled geometric solution. Finally, we also present scenarios where global registration based on feature correspondences fails but multiview ICP based on our robust motion estimation is successful.