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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 541–550 of 876 papers

TitleStatusHype
Depth-Relative Self Attention for Monocular Depth Estimation—0
Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motionCode0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
The Second Monocular Depth Estimation Challenge—0
Self-Supervised Learning based Depth Estimation from Monocular ImagesCode0
SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation—0
TiDy-PSFs: Computational Imaging with Time-Averaged Dynamic Point-Spread-Functions—0
Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenes—0
SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates—0
Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth—0
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