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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 521–530 of 876 papers

TitleStatusHype
Deep 3D Pan via adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Deep Classification Network for Monocular Depth Estimation—0
Deep Depth From Aberration Map—0
Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey—0
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images—0
Deep multi-scale architectures for monocular depth estimation—0
Deep Neural Networks for Accurate Depth Estimation with Latent Space Features—0
Deep Optics for Monocular Depth Estimation and 3D Object Detection—0
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