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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 351–360 of 876 papers

TitleStatusHype
High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimation—0
AutoColor: Learned Light Power Control for Multi-Color HologramsCode0
TaskPrompter: Spatial-Channel Multi-Task Prompting for Dense Scene UnderstandingCode2
Depth-Relative Self Attention for Monocular Depth Estimation—0
A geometry-aware deep network for depth estimation in monocular endoscopyCode1
Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motionCode0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
DINOv2: Learning Robust Visual Features without SupervisionCode6
The Second Monocular Depth Estimation Challenge—0
Self-Supervised Learning based Depth Estimation from Monocular ImagesCode0
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