Learning-Based Distortion Correction and Feature Detection for High Precision and Robust Camera Calibration | VitaVision
PaperIEEE Robotics and Automation Letters · 2022

Learning-Based Distortion Correction and Feature Detection for High Precision and Robust Camera Calibration

Yesheng ZhangXu ZhaoDahong Qian
arXiv 2202.00158DOIView in graph
1page built on it
5more pages cite it
2narratives
0registry papers cite it

In the Atlas

ModelPrimary source of
CCS

Three-stage learning-based camera calibration pipeline: a CNN regresses radial-distortion-correction parameters, a UNet predicts per-corner Gaussian heatmaps refined by surface-fit subpixel localisation, and an image-level RANSAC accepts inlier views before Zhang-style intrinsic estimation.

Cited as a reference by 5 pages, grouped by domain

Calibration 3
CCDNMATEZhang's Planar Camera Calibration
Features 1
Chessboard X-Corner Detection
Image formation 1
Camera Distortion Models

Appears in 2 narratives

NarrativeCalibration Changed the TargetNarrativeFinding a Chessboard

Lineage

Papers in the Atlas registry only · one dot per paper
Built upon by · 0
’87’88’89’90’91’92’93’94’95’96’97’98’99’00’01’02’03’04’05’06’07’08’09’10’11’12’13’14’15’16’17’18’19’20’21’22’23A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses (1987)A Combined Corner and Edge Detector (1988)A Flexible New Technique for Camera Calibration (2000)Using saddle points for subpixel feature detection in camera calibration targets (2003)Automatic camera and range sensor calibration using a single shot (2012)ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration (2014)MATE: Machine Learning for Adaptive Calibration Template Detection (2016)CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration (2023)
Built upon by · 0