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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 341–350 of 876 papers

TitleStatusHype
RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language DescriptionsCode0
DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth EstimationCode0
Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation TechniquesCode0
Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth EstimationCode0
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
On the Benefit of Adversarial Training for Monocular Depth EstimationCode0
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth EstimationCode0
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of MarsCode0
On Robust Cross-View Consistency in Self-Supervised Monocular Depth EstimationCode0
OmniDepth: Dense Depth Estimation for Indoors Spherical PanoramasCode0
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