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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 701–710 of 876 papers

TitleStatusHype
Monocular Depth Parameterizing NetworksCode0
Self-supervised monocular depth estimation from oblique UAV videosCode0
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion—0
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation—0
Variational Monocular Depth Estimation for Reliability Prediction—0
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
Distortion-aware Monocular Depth Estimation for Omnidirectional Images—0
On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation—0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature ExtractionCode0
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