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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 71–80 of 876 papers

TitleStatusHype
A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation—0
RePoseD: Efficient Relative Pose Estimation With Known Depth InformationCode1
Relative Pose Estimation through Affine Corrections of Monocular Depth PriorsCode3
DepthMaster: Taming Diffusion Models for Monocular Depth EstimationCode2
Vision-Language Embodiment for Monocular Depth Estimation—0
Distilling Monocular Foundation Model for Fine-grained Depth Completion—0
GeoDepth: From Point-to-Depth to Plane-to-Depth Modeling for Self-Supervised Monocular Depth Estimation—0
Improved Monocular Depth Prediction Using Distance Transform Over Pre-semantic Contours with Self-supervised Neural Networks—0
MetricDepth: Enhancing Monocular Depth Estimation with Deep Metric Learning—0
Revisiting Monocular 3D Object Detection from Scene-Level Depth Retargeting to Instance-Level Spatial Refinement—0
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