no code implementations • 10 Nov 2023 • Xiaoyi Cai, Siddharth Ancha, Lakshay Sharma, Philip R. Osteen, Bernadette Bucher, Stephen Phillips, Jiuguang Wang, Michael Everett, Nicholas Roy, Jonathan P. How
For uncertainty quantification, we efficiently model both aleatoric and epistemic uncertainty by learning discrete traction distributions and probability densities of the traction predictor's latent features.
1 code implementation • 8 Dec 2020 • Sadat Shaik, Bernadette Bucher, Nephele Agrafiotis, Stephen Phillips, Kostas Daniilidis, William Schmenner
We study style representations learned by neural network architectures incorporating these higher level characteristics.
no code implementations • 25 Sep 2019 • Stephen Phillips, Kostas Daniilidis
In geometric computer vision applications, multi-image feature matching gives more accurate and robust solutions compared to simple two-image matching.
1 code implementation • 7 Jan 2019 • Stephen Phillips, Kostas Daniilidis
Image feature matching is a fundamental part of many geometric computer vision applications, and using multiple images can improve performance.
no code implementations • ICLR 2018 • Andrew Jaegle, Stephen Phillips, Daphne Ippolito, Kostas Daniilidis
Our results demonstrate that this representation is useful for learning motion in the general setting where explicit labels are difficult to obtain.
1 code implementation • 16 Feb 2016 • Andrew Jaegle, Stephen Phillips, Kostas Daniilidis
We propose robust methods for estimating camera egomotion in noisy, real-world monocular image sequences in the general case of unknown observer rotation and translation with two views and a small baseline.