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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 741–750 of 876 papers

TitleStatusHype
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation—0
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey—0
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation—0
RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes—0
Monocular Depth Estimation with Self-supervised Instance Adaptation—0
Adversarial Attacks on Monocular Depth Estimation—0
Monocular Depth Estimation Based On Deep Learning: An Overview—0
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry—0
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation—0
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