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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 671–680 of 876 papers

TitleStatusHype
Self-supervised Learning for Single View Depth and Surface Normal Estimation—0
Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera—0
Self-supervised Monocular Depth Estimation with Large Kernel Attention—0
Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining—0
Self-Supervised Monocular Depth Estimation in the Dark: Towards Data Distribution Compensation—0
Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior—0
Self-Supervised Monocular Scene Decomposition and Depth Estimation—0
Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks—0
Self-Supervised Relative Depth Learning for Urban Scene Understanding—0
SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning—0
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