SOTAVerified

Camera Calibration

Camera calibration involves estimating camera parameters(including camera intrinsics and extrinsics) to infer geometric features from captured sequences, which is crucial for computer vision and robotics. Driven by different architectures of the neural network, the researchers have developed two main paradigms for learning-based camera calibration and its applications. One is Regression-based Calibration,Reconstruction-based Calibration is another.

Papers

Showing 1120 of 343 papers

TitleStatusHype
AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera CalibrationCode2
Simultaneously Recovering Multi-Person Meshes and Multi-View Cameras with Human SemanticsCode2
Boost 3D Reconstruction using Diffusion-based Monocular Camera CalibrationCode2
Enhancing Soccer Camera Calibration Through Keypoint ExploitationCode2
DiffCalib: Reformulating Monocular Camera Calibration as Diffusion-Based Dense Incident Map GenerationCode2
PnLCalib: Sports Field Registration via Points and Lines OptimizationCode2
DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric RenderingCode2
Calib-Anything: Zero-training LiDAR-Camera Extrinsic Calibration Method Using Segment AnythingCode2
Deep Learning for Camera Calibration and Beyond: A SurveyCode2
Perspective Fields for Single Image Camera CalibrationCode2
Show:102550
← PrevPage 2 of 35Next →

No leaderboard results yet.