HyperPosePDF Hypernetworks Predicting the Probability Distribution on SO(3)
Timon Höfer, Benjamin Kiefer, Martin Messmer, Andreas Zell
Abstract
Pose estimation of objects in images is an essential problem in virtual and augmented reality and robotics. Traditional solutions use depth cameras, which can be expensive, and working solutions require long processing times. This work focuses on the more difficult task when only RGB information is available. To this end, we predict not only the pose of an object but the complete probability density function (pdf) on the rotation manifold. This is the most general way to approach the pose estimation problem and is particularly useful in analysing object symmetries. In this work, we leverage implicit neural representations for the task of pose estimation and show that hypernetworks can be used to predict the rotational pdf. Furthermore, we analyse the Fourier embedding on SO(3) and evaluate the effectiveness of an initial Fourier embedding that proved successful. Our HyperPosePDF outperforms the current SOTA approaches on the SYMSOL dataset.