Depth And Camera Motion
13 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Depth And Camera Motion
Most implemented papers
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Models and examples built with TensorFlow
DeMoN: Depth and Motion Network for Learning Monocular Stereo
lmb-freiburg/demon
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CVPR 2017
In this paper we formulate structure from motion as a learning problem.
Unsupervised Learning of Depth and Ego-Motion from Video
tinghuiz/SfMLearner
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CVPR 2017
We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences.
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
tensorflow/models
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CVPR 2018
We present a novel approach for unsupervised learning of depth and ego-motion from monocular video.
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
JiawangBian/sc_depth_pl
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NeurIPS 2019
To the best of our knowledge, this is the first work to show that deep networks trained using unlabelled monocular videos can predict globally scale-consistent camera trajectories over a long video sequence.
Learning Depth from Monocular Videos using Direct Methods
The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community.
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
Huangying-Zhan/Depth-VO-Feat
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CVPR 2018
Despite learning based methods showing promising results in single view depth estimation and visual odometry, most existing approaches treat the tasks in a supervised manner.
BA-Net: Dense Bundle Adjustment Network
frobelbest/BANet
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The network first generates several basis depth maps according to the input image and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA.
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency
vt-vl-lab/DF-Net
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ECCV 2018
We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences.
Sparse Representations for Object and Ego-motion Estimation in Dynamic Scenes
Dynamic scenes that contain both object motion and egomotion are a challenge for monocular visual odometry (VO).
