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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 311–320 of 876 papers

TitleStatusHype
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation—0
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation—0
Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs—0
EdgeConv with Attention Module for Monocular Depth Estimation—0
Composite Learning for Robust and Effective Dense Predictions—0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation—0
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation—0
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes—0
CoL3D: Collaborative Learning of Single-view Depth and Camera Intrinsics for Metric 3D Shape Recovery—0
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