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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 561–570 of 876 papers

TitleStatusHype
Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation—0
Learning Good Features to Transfer Across Tasks and Domains—0
Learning Monocular Depth Estimation via Selective Distillation of Stereo Knowledge—0
Learning Monocular Depth from Events via Egomotion Compensation—0
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention—0
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels—0
Learning Structured Gaussians to Approximate Deep Ensembles—0
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation—0
Learn to Adapt for Monocular Depth Estimation—0
Recovering Detail in 3D Shapes Using Disparity Maps—0
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