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Monocular Depth Estimation

Monocular Depth Estimation is the task of estimating the depth value (distance relative to the camera) of each pixel given a single (monocular) RGB image. This challenging task is a key prerequisite for determining scene understanding for applications such as 3D scene reconstruction, autonomous driving, and AR. State-of-the-art methods usually fall into one of two categories: designing a complex network that is powerful enough to directly regress the depth map, or splitting the input into bins or windows to reduce computational complexity. The most popular benchmarks are the KITTI and NYUv2 datasets. Models are typically evaluated using RMSE or absolute relative error.

Source: Defocus Deblurring Using Dual-Pixel Data

Papers

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Showing 751–800 of 876 papers

TitleStatusHype
Monocular Piecewise Depth Estimation in Dynamic Scenes by Exploiting Superpixel Relations—0
Digging Into Self-Supervised Monocular Depth EstimationCode0
Self-Supervised Monocular Depth HintsCode0
Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled NetworksCode0
Task-Aware Monocular Depth Estimation for 3D Object DetectionCode0
Structure-Attentioned Memory Network for Monocular Depth Estimation—0
UASOL, a large-scale high-resolution outdoor stereo dataset—0
Improving Self-Supervised Single View Depth Estimation by Masking OcclusionCode0
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular VideoCode1
n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error—0
To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation—0
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation—0
Index NetworkCode0
Exploiting temporal consistency for real-time video depth estimationCode0
Enhancing self-supervised monocular depth estimation with traditional visual odometry—0
Semi-Supervised Adversarial Monocular Depth Estimation—0
Adversarial View-Consistent Learning for Monocular Depth Estimation—0
Enforcing geometric constraints of virtual normal for depth predictionCode2
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth EstimationCode1
Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map PredictionCode0
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach—0
Structure-Aware Residual Pyramid Network for Monocular Depth EstimationCode0
Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera—0
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset TransferCode3
SLAM Endoscopy enhanced by adversarial depth prediction—0
Generating and Exploiting Probabilistic Monocular Depth EstimatesCode0
Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics—0
Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation—0
Multimodal End-to-End Autonomous Driving—0
Monocular Depth Estimation Using Relative Depth Maps—0
Connecting the Dots: Learning Representations for Active Monocular Depth Estimation—0
Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding ApproachCode0
Soft Labels for Ordinal Regression—0
Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth—0
Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation—0
SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth EstimationCode0
Semi-Supervised Monocular Depth Estimation with Left-Right Consistency Using Deep Neural NetworkCode0
How do neural networks see depth in single images?—0
PhaseCam3D — Learning Phase Masks for Passive Single View Depth EstimationCode0
Lightweight Monocular Depth Estimation Model by Joint End-to-End Filter pruningCode0
Monocular Depth Estimation with Directional Consistency by Deep Networks—0
3D Packing for Self-Supervised Monocular Depth EstimationCode1
Learn Stereo, Infer Mono: Siamese Networks for Self-Supervised, Monocular, Depth EstimationCode0
A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation—0
Deep Optics for Monocular Depth Estimation and 3D Object Detection—0
Learning Single Camera Depth Estimation using Dual-PixelsCode0
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown CamerasCode0
Learning Across Tasks and DomainsCode0
Learning monocular depth estimation infusing traditional stereo knowledgeCode0
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth EstimationCode0
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