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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 861–870 of 876 papers

TitleStatusHype
Fast Scene Understanding for Autonomous DrivingCode0
Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum InferenceCode0
Recurrent Scene Parsing with Perspective Understanding in the LoopCode0
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth EstimationCode0
Semi-Supervised Deep Learning for Monocular Depth Map Prediction—0
ResearchDoom and CocoDoom: Learning Computer Vision with Games—0
Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional NetworksCode0
A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images—0
Depth Estimation from Single Image using Sparse Representations—0
Dense Monocular Depth Estimation in Complex Dynamic Scenes—0
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