Probabilistic Orientation Estimation with Matrix Fisher Distributions
David Mohlin, Josephine Sullivan, Gérald Bianchi
Abstract
This paper focuses on estimating probability distributions over the set of 3D ro- tations (SO(3)) using deep neural networks. Learning to regress models to the set of rotations is inherently difficult due to differences in topology between R^N and SO(3). We overcome this issue by using a neural network to out- put the parameters for a matrix Fisher distribution since these parameters are homeomorphic to R^9 . By using a negative log likelihood loss for this distri- bution we get a loss which is convex with respect to the network outputs. By optimizing this loss we improve state-of-the-art on several challenging applica- ble datasets, namely Pascal3D+, ModelNet10-SO(3). Our code is available at https://github.com/Davmo049/Public
BibTeX
@inproceedings{NEURIPS2020_33cc2b87,
author = {Mohlin, David and Sullivan, Josephine and Bianchi, G\'{e}rald},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {4884--4893},
publisher = {Curran Associates, Inc.},
title = {Probabilistic Orientation Estimation with Matrix Fisher Distributions},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/33cc2b872dfe481abef0f61af181dfcf-Paper.pdf},
volume = {33},
year = {2020}
}