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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 521–530 of 876 papers

TitleStatusHype
GScream: Learning 3D Geometry and Feature Consistent Gaussian Splatting for Object Removal—0
GSDC Transformer: An Efficient and Effective Cue Fusion for Monocular Multi-Frame Depth Estimation—0
GVDepth: Zero-Shot Monocular Depth Estimation for Ground Vehicles based on Probabilistic Cue Fusion—0
HC-Search for Structured Prediction in Computer Vision—0
Hierarchical Normalization for Robust Monocular Depth Estimation—0
High-fidelity Endoscopic Image Synthesis by Utilizing Depth-guided Neural Surfaces—0
High-Precision Self-Supervised Monocular Depth Estimation with Rich-Resource Prior—0
High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimation—0
HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model—0
How do neural networks see depth in single images?—0
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