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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 476–500 of 876 papers

TitleStatusHype
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery—0
Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion—0
End-to-end Learning for Joint Depth and Image Reconstruction from Diffracted Rotation—0
Enhanced Object Tracking by Self-Supervised Auxiliary Depth Estimation Learning—0
Enhanced Scale-aware Depth Estimation for Monocular Endoscopic Scenes with Geometric Modeling—0
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation—0
Enhancing Diffusion Models with 3D Perspective Geometry Constraints—0
Enhancing self-supervised monocular depth estimation with traditional visual odometry—0
Error Diagnosis of Deep Monocular Depth Estimation Models—0
Estimating Depth of Monocular Panoramic Image with Teacher-Student Model Fusing Equirectangular and Spherical Representations—0
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation—0
EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditions—0
Event-based Monocular Dense Depth Estimation with Recurrent Transformers—0
EventHDR: from Event to High-Speed HDR Videos and Beyond—0
EvLight++: Low-Light Video Enhancement with an Event Camera: A Large-Scale Real-World Dataset, Novel Method, and More—0
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation—0
Extraction of Key-frames of Endoscopic Videos by using Depth Information—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception—0
fCOP: Focal Length Estimation from Category-level Object Priors—0
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles—0
FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation—0
FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation—0
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras—0
FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation—0
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