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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 751–760 of 876 papers

TitleStatusHype
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey—0
Optical Lens Attack on Deep Learning Based Monocular Depth Estimation—0
Optical Lens Attack on Monocular Depth Estimation for Autonomous Driving—0
OrchardDepth: Precise Metric Depth Estimation of Orchard Scene from Monocular Camera Images—0
Outdoor Monocular Depth Estimation: A Research Review—0
P2D: a self-supervised method for depth estimation from polarimetry—0
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation—0
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation—0
Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation—0
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds—0
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