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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 451–460 of 876 papers

TitleStatusHype
The Third Monocular Depth Estimation Challenge—0
Self-Supervised Monocular Depth Estimation in the Dark: Towards Data Distribution Compensation—0
GScream: Learning 3D Geometry and Feature Consistent Gaussian Splatting for Object Removal—0
High-fidelity Endoscopic Image Synthesis by Utilizing Depth-guided Neural Surfaces—0
Virtually Enriched NYU Depth V2 Dataset for Monocular Depth Estimation: Do We Need Artificial Augmentation?Code0
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth EstimationCode0
Into the Fog: Evaluating Robustness of Multiple Object TrackingCode0
Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior—0
Adaptive Discrete Disparity Volume for Self-supervised Monocular Depth Estimation—0
FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation—0
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