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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 51–75 of 876 papers

TitleStatusHype
DDP: Diffusion Model for Dense Visual PredictionCode2
A Simple Framework for 3D Occupancy Estimation in Autonomous DrivingCode2
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth EstimationCode2
Unleashing Text-to-Image Diffusion Models for Visual PerceptionCode2
Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth EstimationCode2
SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic ScenesCode2
Towards Scale-Aware, Robust, and Generalizable Unsupervised Monocular Depth Estimation by Integrating IMU Motion DynamicsCode2
Single-View View Synthesis in the Wild with Learned Adaptive Multiplane ImagesCode2
SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth EstimationCode2
BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationCode2
InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene UnderstandingCode2
NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth EstimationCode2
A Survey on RGB-D DatasetsCode2
360MonoDepth: High-Resolution 360deg Monocular Depth EstimationCode2
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution MergingCode2
Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth PredictionCode2
Learning to Recover 3D Scene Shape from a Single ImageCode2
Semantically-Guided Representation Learning for Self-Supervised Monocular DepthCode2
Enforcing geometric constraints of virtual normal for depth predictionCode2
MonoMVSNet: Monocular Priors Guided Multi-View Stereo NetworkCode1
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised TrainingCode1
TR2M: Transferring Monocular Relative Depth to Metric Depth with Language Descriptions and Scale-Oriented ContrastCode1
Always Clear Depth: Robust Monocular Depth Estimation under Adverse WeatherCode1
DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB ImageCode1
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation ModelCode1
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