CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration

• URL: http://arxiv.org/abs/2302.05097v1
• Date: Fri, 10 Feb 2023 07:47:44 GMT
• Title: CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration
• Authors: Ben Chen, Caihua Xiong, Qi Zhang
• Abstract summary: checkerboard corner detection network and some post-processing techniques. Network model is a fully convolutional network with improvements of loss function and learning rate. In order to remove the false positives, we employ three post-processing techniques including threshold related to maximum response, non-maximum suppression, and clustering.
• Score: 10.614480156920935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aiming to improve the checkerboard corner detection robustness against the images with poor quality, such as lens distortion, extreme poses, and noise, we propose a novel detection algorithm which can maintain high accuracy on inputs under multiply scenarios without any prior knowledge of the checkerboard pattern. This whole algorithm includes a checkerboard corner detection network and some post-processing techniques. The network model is a fully convolutional network with improvements of loss function and learning rate, which can deal with the images of arbitrary size and produce correspondingly-sized output with a corner score on each pixel by efficient inference and learning. Besides, in order to remove the false positives, we employ three post-processing techniques including threshold related to maximum response, non-maximum suppression, and clustering. Evaluations on two different datasets show its superior robustness, accuracy and wide applicability in quantitative comparisons with the state-of-the-art methods, like MATE, ChESS, ROCHADE and OCamCalib.

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