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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
Acquisition of high-quality images for camera calibration in robotics applications via speech prompts0
SoccerNet-v3D: Leveraging Sports Broadcast Replays for 3D Scene UnderstandingCode1
TT3D: Table Tennis 3D Reconstruction0
Implementation of a Zed 2i Stereo Camera for High-Frequency Shoreline Change and Coastal Elevation Monitoring0
DF-Calib: Targetless LiDAR-Camera Calibration via Depth Flow0
Robust LiDAR-Camera Calibration with 2D Gaussian SplattingCode1
AlignDiff: Learning Physically-Grounded Camera Alignment via Diffusion0
AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera CalibrationCode2
MUSt3R: Multi-view Network for Stereo 3D ReconstructionCode3
Blind Augmentation: Calibration-free Camera Distortion Model Estimation for Real-time Mixed-reality ConsistencyCode0
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