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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 551–560 of 876 papers

TitleStatusHype
KineDepth: Utilizing Robot Kinematics for Online Metric Depth Estimation—0
LAA-Net: A Physical-prior-knowledge Based Network for Robust Nighttime Depth Estimation—0
Language-Based Depth Hints for Monocular Depth Estimation—0
Large Language Models Can Understanding Depth from Monocular Images—0
Large-scale Monocular Depth Estimation in the Wild—0
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images—0
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss—0
Learning depth from monocular video sequences—0
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach—0
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance—0
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