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 551–560 of 876 papers

TitleStatusHype
A Simple Baseline for Supervised Surround-view Depth Estimation—0
DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation—0
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation—0
Monocular Depth Estimation using Diffusion Models—0
Bokeh Rendering Based on Adaptive Depth Calibration Network—0
Depth Estimation and Image Restoration by Deep Learning from Defocused Images—0
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images—0
Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset RefinementCode0
On the Metrics for Evaluating Monocular Depth Estimation—0
GlocalFuse-Depth: Fusing Transformers and CNNs for All-day Self-supervised Monocular Depth Estimation—0
Show:102550
← PrevPage 56 of 88Next →

No leaderboard results yet.