Camera Calibration
125 papers with code • 0 benchmarks • 4 datasets
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.
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Most implemented papers
Improvements to Target-Based 3D LiDAR to Camera Calibration
The homogeneous transformation between a LiDAR and monocular camera is required for sensor fusion tasks, such as SLAM.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
LiDAR-Camera Calibration using 3D-3D Point correspondences
With the advent of autonomous vehicles, LiDAR and cameras have become an indispensable combination of sensors.
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems.
Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation
Homography estimation is an important task in computer vision applications, such as image stitching, video stabilization, and camera calibration.
DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations
With the recent development of Deep Learning applied to Computer Vision, sport video understanding has gained a lot of attention, providing much richer information for both sport consumers and leagues.
Sports Camera Calibration via Synthetic Data
Here we propose a highly automatic method for calibrating sports cameras from a single image using synthetic data.
Leveraging blur information for plenoptic camera calibration
This paper presents a novel calibration algorithm for plenoptic cameras, especially the multi-focus configuration, where several types of micro-lenses are used, using raw images only.
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
We present a novel approach for vanishing point detection from uncalibrated monocular images.
CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
During training, the network only takes as input a LiDAR point cloud, the corresponding monocular image, and the camera calibration matrix K. At train time, we do not impose direct supervision (i. e., we do not directly regress to the calibration parameters, for example).