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 301–310 of 876 papers

TitleStatusHype
GEDepth: Ground Embedding for Monocular Depth EstimationCode1
Deep Neighbor Layer Aggregation for Lightweight Self-Supervised Monocular Depth EstimationCode0
X-PDNet: Accurate Joint Plane Instance Segmentation and Monocular Depth Estimation with Cross-Task Distillation and Boundary CorrectionCode1
Towards Better Data Exploitation in Self-Supervised Monocular Depth EstimationCode1
Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth EstimationCode1
SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth EstimationCode1
Learning to Upsample by Learning to SampleCode1
Real-time Monocular Depth Estimation on Embedded Systems—0
AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization—0
Robust Monocular Depth Estimation under Challenging Conditions—0
Show:102550
← PrevPage 31 of 88Next →

No leaderboard results yet.