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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 726–750 of 876 papers

TitleStatusHype
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse pointsCode1
Monocular Depth Estimation Based On Deep Learning: An Overview—0
DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular VideosCode1
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry—0
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
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation—0
Semantically-Guided Representation Learning for Self-Supervised Monocular DepthCode2
Dense monocular Simultaneous Localization and Mapping by direct surfel optimization—0
FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation—0
Aerial Single-View Depth Completion with Image-Guided Uncertainty EstimationCode1
Single Image Depth Estimation Trained via Depth from Defocus CuesCode1
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey—0
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video—0
Instance-wise Depth and Motion Learning from Monocular VideosCode1
Edge-Guided Occlusion Fading Reduction for a Light-Weighted Self-Supervised Monocular Depth EstimationCode0
Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method—0
On the Benefit of Adversarial Training for Monocular Depth EstimationCode0
Deep Classification Network for Monocular Depth Estimation—0
Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments—0
Unsupervised High-Resolution Depth Learning From Videos With Dual Networks—0
ClearGrasp: 3D Shape Estimation of Transparent Objects for ManipulationCode0
Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances—0
Deep 3D Pan via adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Deep Depth From Aberration Map—0
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