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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 601–625 of 876 papers

TitleStatusHype
Exploring Depth Contribution for Camouflaged Object Detection—0
EdgeConv with Attention Module for Monocular Depth Estimation—0
A Hybrid mmWave and Camera System for Long-Range Depth Imaging—0
Single Image Depth Prediction with Wavelet DecompositionCode1
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution MergingCode2
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile—0
Unsupervised Scale-consistent Depth Learning from VideoCode1
M4Depth: Monocular depth estimation for autonomous vehicles in unseen environmentsCode1
Learning to Relate Depth and Semantics for Unsupervised Domain AdaptationCode1
Boosting Light-Weight Depth Estimation Via Knowledge DistillationCode1
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular DepthCode1
Domain Adaptive Monocular Depth Estimation With Semantic Information—0
Improving Online Performance Prediction for Semantic Segmentation—0
Learning optical flow from still imagesCode1
S2R-DepthNet: Learning a Generalizable Depth-specific Structural RepresentationCode1
Deep Two-View Structure-from-Motion RevisitedCode1
Geometric Unsupervised Domain Adaptation for Semantic Segmentation—0
Vision Transformers for Dense PredictionCode3
SaccadeCam: Adaptive Visual Attention for Monocular Depth SensingCode0
Revisiting Self-Supervised Monocular Depth EstimationCode0
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-SupervisionCode1
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss—0
Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth PredictionCode2
Implicit Integration of Superpixel Segmentation into Fully Convolutional NetworksCode1
Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth EstimationCode0
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