Corner-based geometric calibration of multi-focus plenoptic cameras

TY - JOUR

T1 - Corner-based geometric calibration of multi-focus plenoptic cameras

AU - Nousias, Sotiris

AU - Chadebecq, Francois

AU - Pichat, Jonas

AU - Keane, Pearse

AU - Ourselin, Sebastien

AU - Bergeles, Christos

PY - 2017/12/25

Y1 - 2017/12/25

N2 - We propose a method for geometric calibration of multifocus plenoptic cameras using raw images. Multi-focus plenoptic cameras feature several types of micro-lenses spatially aligned in front of the camera sensor to generate micro-images at different magnifications. This multi-lens arrangement provides computational-photography benefits but complicates calibration. Our methodology achieves the detection of the type of micro-lenses, the retrieval of their spatial arrangement, and the estimation of intrinsic and extrinsic camera parameters therefore fully characterising this specialised camera class. Motivated from classic pinhole camera calibration, our algorithm operates on a checker-board's corners, retrieved by a custom microimage corner detector. This approach enables the introduction of a reprojection error that is used in a minimisation framework. Our algorithm compares favourably to the state-of-the-art, as demonstrated by controlled and freehand experiments, making it a first step towards accurate 3D reconstruction and Structure-from-Motion.

AB - We propose a method for geometric calibration of multifocus plenoptic cameras using raw images. Multi-focus plenoptic cameras feature several types of micro-lenses spatially aligned in front of the camera sensor to generate micro-images at different magnifications. This multi-lens arrangement provides computational-photography benefits but complicates calibration. Our methodology achieves the detection of the type of micro-lenses, the retrieval of their spatial arrangement, and the estimation of intrinsic and extrinsic camera parameters therefore fully characterising this specialised camera class. Motivated from classic pinhole camera calibration, our algorithm operates on a checker-board's corners, retrieved by a custom microimage corner detector. This approach enables the introduction of a reprojection error that is used in a minimisation framework. Our algorithm compares favourably to the state-of-the-art, as demonstrated by controlled and freehand experiments, making it a first step towards accurate 3D reconstruction and Structure-from-Motion.

UR - https://www.scopus.com/pages/publications/85041910390

U2 - 10.1109/ICCV.2017.109

DO - 10.1109/ICCV.2017.109

M3 - Conference paper

AN - SCOPUS:85041910390

VL - 2017-October

SP - 957

EP - 965

JO - 2017 IEEE International Conference on Computer Vision (ICCV)

JF - 2017 IEEE International Conference on Computer Vision (ICCV)

T2 - 16th IEEE International Conference on Computer Vision, ICCV 2017

Y2 - 22 October 2017 through 29 October 2017

ER -