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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 371–380 of 876 papers

TitleStatusHype
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions—0
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth—0
Analysis of NaN Divergence in Training Monocular Depth Estimation Model—0
Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method—0
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels—0
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation—0
Improving the Reliability for Confidence Estimation—0
Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth—0
An Advert Creation System for 3D Product Placements—0
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion—0
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