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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 251–300 of 876 papers

TitleStatusHype
Detecting Invisible PeopleCode1
HR-Depth: High Resolution Self-Supervised Monocular Depth EstimationCode1
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic SegmentationCode1
AdaBins: Depth Estimation using Adaptive BinsCode1
Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive LossCode1
Unsupervised Monocular Depth Learning in Dynamic ScenesCode1
Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce ModelCode1
DepthLab: Real-Time 3D Interaction With Depth Maps for Mobile Augmented RealityCode1
Unsupervised Monocular Depth Estimation for Night-time Images using Adversarial Domain Feature AdaptationCode1
Adaptive confidence thresholding for monocular depth estimationCode1
Multi-Loss Weighting with Coefficient of VariationsCode1
Bidirectional Attention Network for Monocular Depth EstimationCode1
One Shot 3D PhotographyCode1
Self-Supervised Learning for Monocular Depth Estimation from Aerial ImageryCode1
Learning Stereo from Single ImagesCode1
Multi-Loss Rebalancing Algorithm for Monocular Depth EstimationCode1
Pixel-Pair Occlusion Relationship Map(P2ORM): Formulation, Inference & ApplicationCode1
Feature-metric Loss for Self-supervised Learning of Depth and EgomotionCode1
P^2Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth EstimationCode1
Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic GuidanceCode1
Regression Prior NetworksCode1
Targeted Adversarial Perturbations for Monocular Depth PredictionCode1
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry EstimationCode1
Auto-Rectify Network for Unsupervised Indoor Depth EstimationCode1
Robust Learning Through Cross-Task ConsistencyCode1
Structure-Guided Ranking Loss for Single Image Depth PredictionCode1
On the uncertainty of self-supervised monocular depth estimationCode1
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
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse pointsCode1
DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular VideosCode1
Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D GeometryCode1
Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement FieldsCode1
Aerial Single-View Depth Completion with Image-Guided Uncertainty EstimationCode1
Single Image Depth Estimation Trained via Depth from Defocus CuesCode1
Instance-wise Depth and Motion Learning from Monocular VideosCode1
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular VideoCode1
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth EstimationCode1
3D Packing for Self-Supervised Monocular Depth EstimationCode1
High Quality Monocular Depth Estimation via Transfer LearningCode1
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary CellsCode1
Towards real-time unsupervised monocular depth estimation on CPUCode1
Deep Ordinal Regression Network for Monocular Depth EstimationCode1
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