3 citations · 6 across the 3 of their papers we have counts for
4 papers · 1 filter
ZeroVO: Visual Odometry with Minimal Assumptions
Lei Lai, Zekai Yin, Eshed Ohn-Bar
We introduce ZeroVO, a novel visual odometry (VO) algorithm that achieves zero-shot generalization across diverse cameras and environments, overcoming limitations in existing metho…
XVO: Generalized Visual Odometry via Cross-Modal Self-Training
Lei Lai, Zhongkai Shangguan, Jimuyang Zhang +1
We propose XVO, a semi-supervised learning method for training generalized monocular Visual Odometry (VO) models with robust off-the-self operation across diverse datasets and sett…
Learning to Drive Anywhere
Ruizhao Zhu, Peng Huang, Eshed Ohn-Bar +1
Human drivers can seamlessly adapt their driving decisions across geographical locations with diverse conditions and rules of the road, e.g., left vs. right-hand traffic. In contra…
To Boost or Not to Boost? On the Limits of Boosted Trees for Object Detection
Eshed Ohn-Bar, Mohan M. Trivedi
We aim to study the modeling limitations of the commonly employed boosted decision trees classifier. Inspired by the success of large, data-hungry visual recognition models (e.g. d…