Unsupervised Monocular Depth Estimation
36 papers with code • 6 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
Unsupervised Monocular Depth Estimation with Left-Right Consistency
mrharicot/monodepth
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CVPR 2017
Learning based methods have shown very promising results for the task of depth estimation in single images.
Digging Into Self-Supervised Monocular Depth Estimation
Per-pixel ground-truth depth data is challenging to acquire at scale.
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Models and examples built with TensorFlow
Unsupervised Monocular Depth Learning in Dynamic Scenes
google-research/google-research
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We present a method for jointly training the estimation of depth, ego-motion, and a dense 3D translation field of objects relative to the scene, with monocular photometric consistency being the sole source of supervision.
Towards real-time unsupervised monocular depth estimation on CPU
mattpoggi/pydnet
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To tackle this issue, in this paper we propose a novel architecture capable to quickly infer an accurate depth map on a CPU, even of an embedded system, using a pyramid of features extracted from a single input image.
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras
google-research/google-research
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ICCV 2019
We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as supervision signal.
A General and Adaptive Robust Loss Function
google-research/google-research
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CVPR 2019
We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions.
SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes
Self-supervised monocular depth estimation has shown impressive results in static scenes.
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes
Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics.
Dual CNN Models for Unsupervised Monocular Depth Estimation
ishmav16/Dual-CNN-Models-for-Unsupervised-Monocular-Depth-Estimation
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The unsupervised depth estimation is the recent trend by utilizing the binocular stereo images to get rid of depth map ground truth.
