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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 626–650 of 876 papers

TitleStatusHype
CroMo: Cross-Modal Learning for Monocular Depth Estimation—0
Distortion-Tolerant Monocular Depth Estimation On Omnidirectional Images Using Dual-cubemap—0
Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation—0
SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning—0
Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
How Much Depth Information can Radar Contribute to a Depth Estimation Model?—0
Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion—0
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression—0
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks—0
Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings—0
Maximum Likelihood Uncertainty Estimation: Robustness to OutliersCode0
Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth—0
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation—0
GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding—0
Multi-Robot Collaborative Perception with Graph Neural Networks—0
Neural Window Fully-Connected CRFs for Monocular Depth Estimation—0
Generalizing Interactive Backpropagating Refinement for Dense Prediction Networks—0
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation—0
Improving Depth Estimation using Location Information—0
NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis—0
Towards Comprehensive Monocular Depth Estimation: Multiple Heads Are Better Than One—0
Error Diagnosis of Deep Monocular Depth Estimation Models—0
Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation—0
CamLessMonoDepth: Monocular Depth Estimation with Unknown Camera Parameters—0
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