SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 321–330 of 876 papers

TitleStatusHype
ClearGrasp: 3D Shape Estimation of Transparent Objects for ManipulationCode0
On the Viability of Monocular Depth Pre-training for Semantic SegmentationCode0
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object BoundariesCode0
D-Net: A Generalised and Optimised Deep Network for Monocular Depth EstimationCode0
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric AnnotationsCode0
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on RegressionCode0
Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motionCode0
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional ArchitectureCode0
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic SegmentationCode0
Digging Into Self-Supervised Monocular Depth EstimationCode0
Show:102550
← PrevPage 33 of 88Next →

No leaderboard results yet.