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 561–570 of 876 papers

TitleStatusHype
Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic SegmentationCode0
Monocular Depth Estimation with Sharp Boundary—0
D-Net: A Generalised and Optimised Deep Network for Monocular Depth EstimationCode0
f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception—0
Excavating the Potential Capacity of Self-Supervised Monocular Depth EstimationCode1
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data—0
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised LearningCode1
Advancing Self-supervised Monocular Depth Learning with Sparse LiDARCode1
Show:102550
← PrevPage 57 of 88Next →

No leaderboard results yet.