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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 481–490 of 876 papers

TitleStatusHype
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation—0
Enhancing Diffusion Models with 3D Perspective Geometry Constraints—0
Enhancing self-supervised monocular depth estimation with traditional visual odometry—0
Error Diagnosis of Deep Monocular Depth Estimation Models—0
Estimating Depth of Monocular Panoramic Image with Teacher-Student Model Fusing Equirectangular and Spherical Representations—0
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation—0
EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditions—0
Event-based Monocular Dense Depth Estimation with Recurrent Transformers—0
EventHDR: from Event to High-Speed HDR Videos and Beyond—0
EvLight++: Low-Light Video Enhancement with an Event Camera: A Large-Scale Real-World Dataset, Novel Method, and More—0
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