Search Results for author:
Found 3 papers, 2 papers with code
UniDepth: Universal Monocular Metric Depth Estimation
However, the remarkable accuracy of recent MMDE methods is confined to their training domains.
Ranked #2 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
iDisc: Internal Discretization for Monocular Depth Estimation
Our method sets the new state of the art with significant improvements on NYU-Depth v2 and KITTI, outperforming all published methods on the official KITTI benchmark.
Ranked #3 on
Surface Normals Estimation
on NYU Depth v2
Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains
Methods for unsupervised domain adaptation (UDA) help to improve the performance of deep neural networks on unseen domains without any labeled data.
