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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 401–425 of 876 papers

TitleStatusHype
Improving Online Performance Prediction for Semantic Segmentation—0
Language-Based Depth Hints for Monocular Depth Estimation—0
Improving Monocular Visual Odometry Using Learned Depth—0
Large Language Models Can Understanding Depth from Monocular Images—0
Large-scale Monocular Depth Estimation in the Wild—0
DepthFake: a depth-based strategy for detecting Deepfake videos—0
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images—0
Depth Priors in Removal Neural Radiance Fields—0
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss—0
Look to Locate: Vision-Based Multisensory Navigation with 3-D Digital Maps for GNSS-Challenged Environments—0
Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets—0
Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training—0
Depth Estimation with Simplified Transformer—0
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance—0
Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN—0
Depth Estimation from Single Image using Sparse Representations—0
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency—0
Depth-Relative Self Attention for Monocular Depth Estimation—0
Improved Monocular Depth Prediction Using Distance Transform Over Pre-semantic Contours with Self-supervised Neural Networks—0
Learning Monocular Depth Estimation via Selective Distillation of Stereo Knowledge—0
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation—0
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment—0
Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs—0
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