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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 701–725 of 876 papers

TitleStatusHype
Robust Learning Through Cross-Task ConsistencyCode1
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
Structure-Guided Ranking Loss for Single Image Depth PredictionCode1
Monocular Depth Estimators: Vulnerabilities and Attacks—0
On the uncertainty of self-supervised monocular depth estimationCode1
VisualEchoes: Spatial Image Representation Learning through Echolocation—0
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Deflating Dataset Bias Using Synthetic Data Augmentation—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation—0
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey—0
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation—0
RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes—0
Monocular Depth Estimation with Self-supervised Instance Adaptation—0
Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving ApplicationsCode1
Self-Supervised Monocular Scene Flow EstimationCode1
Guiding Monocular Depth Estimation Using Depth-Attention VolumeCode1
Towards Better Generalization: Joint Depth-Pose Learning without PoseNetCode1
The Edge of Depth: Explicit Constraints between Segmentation and DepthCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity VolumeCode1
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation LearningCode1
Holopix50k: A Large-Scale In-the-wild Stereo Image DatasetCode1
Adversarial Attacks on Monocular Depth Estimation—0
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