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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 461–470 of 876 papers

TitleStatusHype
Adversarial Attacks on Monocular Depth Estimation—0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
Adversarial View-Consistent Learning for Monocular Depth Estimation—0
A Hybrid mmWave and Camera System for Long-Range Depth Imaging—0
A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation—0
Align3R: Aligned Monocular Depth Estimation for Dynamic Videos—0
AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization—0
A Multi-modal Approach to Single-modal Visual Place Classification—0
An Advert Creation System for 3D Product Placements—0
Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method—0
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