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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 651–700 of 876 papers

TitleStatusHype
Variational Monocular Depth Estimation for Reliability Prediction—0
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
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
Distortion-aware Monocular Depth Estimation for Omnidirectional Images—0
On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation—0
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature ExtractionCode0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
Unsupervised Monocular Depth Estimation for Night-time Images using Adversarial Domain Feature AdaptationCode1
Monocular Differentiable Rendering for Self-Supervised 3D Object Detection—0
Adaptive confidence thresholding for monocular depth estimationCode1
Towards General Purpose Geometry-Preserving Single-View Depth Estimation—0
Calibrating Self-supervised Monocular Depth Estimation—0
Cascade Network for Self-Supervised Monocular Depth Estimation—0
Monocular Depth Estimation Using Multi Scale Neural Network And Feature Fusion—0
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency—0
Multi-Loss Weighting with Coefficient of VariationsCode1
Bidirectional Attention Network for Monocular Depth EstimationCode1
One Shot 3D PhotographyCode1
Exploring the Impacts from Datasets to Monocular Depth Estimation (MDE) Models with MineNavi—0
Self-Supervised Learning for Monocular Depth Estimation from Aerial ImageryCode1
Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP—0
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving—0
Learning Stereo from Single ImagesCode1
Pixel-Pair Occlusion Relationship Map (P2ORM): Formulation, Inference & Application—0
Multi-Loss Rebalancing Algorithm for Monocular Depth EstimationCode1
Disambiguating Monocular Depth Estimation with a Single Transient—0
CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
Pixel-Pair Occlusion Relationship Map(P2ORM): Formulation, Inference & ApplicationCode1
Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets—0
Mobile3DRecon: Real-time Monocular 3D Reconstruction on a Mobile Phone—0
Feature-metric Loss for Self-supervised Learning of Depth and EgomotionCode1
P2D: a self-supervised method for depth estimation from polarimetry—0
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
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models—0
Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy—0
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation—0
An Advert Creation System for 3D Product Placements—0
Increased-Range Unsupervised Monocular Depth Estimation—0
Regression Prior NetworksCode1
Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues—0
AcED: Accurate and Edge-consistent Monocular Depth Estimation—0
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
PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation—0
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