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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 761–770 of 876 papers

TitleStatusHype
To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation—0
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation—0
Index NetworkCode0
Exploiting temporal consistency for real-time video depth estimationCode0
Enhancing self-supervised monocular depth estimation with traditional visual odometry—0
Semi-Supervised Adversarial Monocular Depth Estimation—0
Adversarial View-Consistent Learning for Monocular Depth Estimation—0
Enforcing geometric constraints of virtual normal for depth predictionCode2
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth EstimationCode1
Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map PredictionCode0
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