Unsupervised Monocular Depth Estimation
35 papers with code • 5 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Unsupervised Monocular Depth Estimation
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Cityscapes
KITTI
KITTI-C
CamlessVideosFromTheWild
Most implemented papers
The Edge of Depth: Explicit Constraints between Segmentation and Depth
TWJianNuo/EdgeDepth-Release
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CVPR 2020
In this work we study the mutual benefits of two common computer vision tasks, self-supervised depth estimation and semantic segmentation from images.
Unsupervised Monocular Depth Estimation for Night-time Images using Adversarial Domain Feature Adaptation
We propose to solve this problem by posing it as a domain adaptation problem where a network trained with day-time images is adapted to work for night-time images.
HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation
shawLyu/HR-Depth
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To obtainmore accurate depth estimation in large gradient regions, itis necessary to obtain high-resolution features with spatialand semantic information.
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency
We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision.
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth
nianticlabs/manydepth
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CVPR 2021
We propose ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available.
Unsupervised Monocular Depth Estimation in Highly Complex Environments
Meanwhile, we further tackle the effects of unstable image transfer quality on domain adaptation, and an image adaptation approach is proposed to evaluate the quality of transferred images and re-weight the corresponding losses, so as to improve the performance of the adapted depth model.
Self-Supervised Monocular Depth Estimation with Internal Feature Fusion
Therefore, it is natural to exploit semantic segmentation networks for depth estimation.
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth
Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions.
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth Maps
Much of the recent work focuses on improving depth estimation by increasing architecture complexity.
Towards Scale-Aware, Robust, and Generalizable Unsupervised Monocular Depth Estimation by Integrating IMU Motion Dynamics
senzhang-github/ekf-imu-depth
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Unsupervised monocular depth and ego-motion estimation has drawn extensive research attention in recent years.
