Camera calibration is an indispensable step in the fields of robotics and computer vision, which includes augmented reality, 3D reconstruction, and camera motion estimation. Before camera calibration, detecting matching correspondence is necessary to understand the structure of the world from multiple images. For an accurate result, a calibration object, such as a chessboard, is used. Existing handcrafted feature methods precisely detect chessboard corners but are weak against blurs, noises, and severe lens distortion. Conversely, neural network-based methods can detect corners regardless of noises in the image. Both methods do not utilize the information of camera priors, which are lens distortion and intrinsic parameters, affecting the location of chessboard corners. Learning of lens distortion and intrinsic parameters enables the proposed network to understand the alignment of corners more precisely. Therefore, in this paper, we propose a novel multi-task learning framework to detect chessboard corners and simultaneously estimate lens distortion and intrinsic parameters. In order to train these three tasks, synthetic images of the chessboard are generated with ground-truth labels corresponding to each task. Hence, by learning the camera priors, the proposed network can more precisely locate the corners than other state-of-the-art corner detection methods while robust to noises, blurs, and distortion.
|Title of host publication||IEEE 23rd International Workshop on Multimedia Signal Processing, MMSP 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2021|
|Event||23rd IEEE International Workshop on Multimedia Signal Processing, MMSP 2021 - Tampere, Finland|
Duration: 2021 Oct 6 → 2021 Oct 8
|Name||IEEE 23rd International Workshop on Multimedia Signal Processing, MMSP 2021|
|Conference||23rd IEEE International Workshop on Multimedia Signal Processing, MMSP 2021|
|Period||21/10/6 → 21/10/8|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea Government (Ministry of Science and ICT, MSIT) under Grant NRF-2020R1A2C3011697
© 2021 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Safety, Risk, Reliability and Quality
- Media Technology