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 451–460 of 876 papers

TitleStatusHype
3D Object Aided Self-Supervised Monocular Depth Estimation—0
AcED: Accurate and Edge-consistent Monocular Depth Estimation—0
A Compromise Principle in Deep Monocular Depth Estimation—0
A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose—0
A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation—0
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation—0
AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation—0
Adaptive Discrete Disparity Volume for Self-supervised Monocular Depth Estimation—0
ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation—0
Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy—0
Show:102550
← PrevPage 46 of 88Next →

No leaderboard results yet.