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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 651–675 of 876 papers

TitleStatusHype
LAA-Net: A Physical-prior-knowledge Based Network for Robust Nighttime Depth Estimation—0
Language-Based Depth Hints for Monocular Depth Estimation—0
Large Language Models Can Understanding Depth from Monocular Images—0
Large-scale Monocular Depth Estimation in the Wild—0
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images—0
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss—0
Learning depth from monocular video sequences—0
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach—0
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance—0
Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation—0
Learning Good Features to Transfer Across Tasks and Domains—0
Learning Monocular Depth Estimation via Selective Distillation of Stereo Knowledge—0
Learning Monocular Depth from Events via Egomotion Compensation—0
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention—0
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels—0
Learning Structured Gaussians to Approximate Deep Ensembles—0
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation—0
Learn to Adapt for Monocular Depth Estimation—0
Recovering Detail in 3D Shapes Using Disparity Maps—0
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos—0
Leveraging Stable Diffusion for Monocular Depth Estimation via Image Semantic Encoding—0
Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers—0
LighthouseGS: Indoor Structure-aware 3D Gaussian Splatting for Panorama-Style Mobile Captures—0
Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion—0
EndoDepthL: Lightweight Endoscopic Monocular Depth Estimation with CNN-Transformer—0
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