Learning Monocular Depth by Distilling Cross-Domain Stereo Networks (original) (raw)

Learn to Adapt for Monocular Depth Estimation

Gary Yen

Cornell University - arXiv, 2022

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Toward Domain Independence for Learning-Based Monocular Depth Estimation

Paolo Valigi

IEEE Robotics and Automation Letters, 2017

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RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes

Armin Mustafa

ArXiv, 2020

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DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning

Simon Hadfield

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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DISCO: Depth Inference from Stereo using Context

Kaushik Raghavan

2019

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CNN Based Monocular Depth Estimation

Hima Valiveti

E3S Web of Conferences, 2021

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StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction

Julien Valentin

Lecture Notes in Computer Science, 2018

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StereoNet: Guided Hierarchical Refinement for Edge-Aware Depth Prediction

Julien Valentin

2018

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Deep Classification Network for Monocular Depth Estimation

Azeez Oluwafemi

2019

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Self-Supervised Correlational Monocular Depth Estimation using ResVGG Network

Kuo Shiuan Peng

Proceedings of The 7th International Conference on Intelligent Systems and Image Processing 2019

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X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation

shubhankar borse

2021

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SUW-Learn: Joint Supervised, Unsupervised, Weakly Supervised Deep Learning for Monocular Depth Estimation

Aman Raj

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020

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DeMoN: Depth and Motion Network for Learning Monocular Stereo

Jonas Uhrig

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

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A Lightweight Self-Supervised Training Framework for Monocular Depth Estimation

Shan Du

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Monocular Depth Estimation using Transfer learning-An Overview

Chaitanya Duggineni

E3S Web of Conferences

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Ing for Self-Supervised Monocular Depth

Vitor Guizilini

2020

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FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth Completion

Dominik Hirner

Cornell University - arXiv, 2022

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Unpaired Learning of Dense Visual Depth Estimators for Urban Environments

Vitor Guizilini

Conference on Robot Learning, 2018

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ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation

Vinay Kaushik

IEEE Robotics and Automation Letters, 2021

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Monocular Depth Estimation Using Deep Learning: A Review

Domenec Puig

Sensors

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Self-Supervised Learning of Domain Invariant Features for Depth Estimation

Shariq Bhat

2021

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Semantically-Guided Representation Learning for Self-Supervised Monocular Depth

Vitor Guizilini

2020

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Multi-Frame Self-Supervised Depth with Transformers

Vitor Guizilini

ArXiv, 2022

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Achieving Domain Robustness in Stereo Matching Networks by Removing Shortcut Learning

Ruwan Tennakoon

ArXiv, 2021

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NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis

Martin R. Oswald

2021 International Conference on 3D Vision (3DV), 2021

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Long Range Stereo Matching by Learning Depth and Disparity

Ruwan Tennakoon

ArXiv, 2020

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Monocular Depth Estimation using Adversarial Training

Pallavi Mitra

University of Minnesota, 2020

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Cascade Network for Self-Supervised Monocular Depth Estimation

Chunlai Chai

ArXiv, 2020

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Learning Single Camera Depth Estimation Using Dual-Pixels

Sameer Ansari

2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019

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Domain-Invariant Stereo Matching Networks

Benjamin Wah

Lecture Notes in Computer Science, 2020

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The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth

Jamie Watson

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021

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