Publications (6)
All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks
Stephen Phillips, Kostas Daniilidis
Image feature matching is a fundamental part of many geometric computer vision applications, and using multiple images can improve performance. In this work, we formulate multi-ima…
Fast, Robust, Continuous Monocular Egomotion Computation
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 v…
Understanding image motion with group representations
Andrew Jaegle, Stephen Phillips, Daphne Ippolito +1
Motion is an important signal for agents in dynamic environments, but learning to represent motion from unlabeled video is a difficult and underconstrained problem. We propose a mo…
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
Xiaoyi Cai, Siddharth Ancha, Lakshay Sharma +7
Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead of manually designing costs based on terrain features, existing methods learn terra…
Learning Portrait Style Representations
Sadat Shaik, Bernadette Bucher, Nephele Agrafiotis +3
Style analysis of artwork in computer vision predominantly focuses on achieving results in target image generation through optimizing understanding of low level style characteristi…
Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation
John Z. Zhang, Maks Sorokin, Jan Brüdigam +14
This paper presents a sim-to-real approach that enables legged robots to dynamically manipulate large and heavy objects with whole-body dexterity. Our key insight is that by perfor…