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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 776–800 of 876 papers

TitleStatusHype
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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
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
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
Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation—0
Monocular Depth Estimation Using Relative Depth Maps—0
Soft Labels for Ordinal Regression—0
Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding ApproachCode0
Connecting the Dots: Learning Representations for Active Monocular Depth Estimation—0
Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth—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
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