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 421–430 of 876 papers

TitleStatusHype
Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN—0
Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention—0
Depth Estimation from Single Image using Sparse Representations—0
Improved Monocular Depth Prediction Using Distance Transform Over Pre-semantic Contours with Self-supervised Neural Networks—0
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
Look to Locate: Vision-Based Multisensory Navigation with 3-D Digital Maps for GNSS-Challenged Environments—0
Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs—0
Depth Estimation Based on 3D Gaussian Splatting Siamese Defocus—0
Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces—0
Show:102550
← PrevPage 43 of 88Next →

No leaderboard results yet.