PointNetLK Revisited
We revisit a recent innovation -- PointNetLK -- and show that the inclusion of an analytical Jacobian
can exhibit remarkable generalization properties while reaping the inherent fidelity benefits
of a learning framework. Our approach not only outperforms the state-of-the-art in mismatched conditions
but also produces results competitive with current learning methods when operating on real-world test data close to the training set.