RPSRNet: End-to-End Trainable Rigid Point Set Registration Network Using Barnes-Hut 2D-Tree Representation

no code implementations • CVPR 2021 • Sk Aziz Ali, Kerem Kahraman, Gerd Reis, Didier Stricker

For this task, we use a novel 2^D-tree representation for the input point sets and a hierarchical deep feature embedding in the neural network.

RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut $2^D$-Tree Representation

no code implementations • 12 Apr 2021 • Sk Aziz Ali, Kerem Kahraman, Gerd Reis, Didier Stricker

For this task, we use a novel $2^D$-tree representation for the input point sets and a hierarchical deep feature embedding in the neural network.

Fast Gravitational Approach for Rigid Point Set Registration with Ordinary Differential Equations

no code implementations • 28 Sep 2020 • Sk Aziz Ali, Kerem Kahraman, Christian Theobalt, Didier Stricker, Vladislav Golyanik

This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA).