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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 491–500 of 876 papers

TitleStatusHype
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation—0
Extraction of Key-frames of Endoscopic Videos by using Depth Information—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception—0
fCOP: Focal Length Estimation from Category-level Object Priors—0
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles—0
FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation—0
FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation—0
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras—0
FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation—0
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