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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 841–850 of 876 papers

TitleStatusHype
Deep multi-scale architectures for monocular depth estimation—0
Monocular Depth Estimation with Augmented Ordinal Depth Relationships—0
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion SegmentationCode0
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images—0
Adversarial Structure Matching for Structured Prediction TasksCode0
Dual CNN Models for Unsupervised Monocular Depth EstimationCode0
Estimating Depth from RGB and Sparse SensingCode0
Structured Attention Guided Convolutional Neural Fields for Monocular Depth EstimationCode0
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object BoundariesCode0
Monocular Depth Estimation by Learning from Heterogeneous Datasets—0
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