Equal Baseline Camera Array-Calibration, Testbed and Applications

This paper presents research on 3D scanning by taking advantage of a camera array consisting of up to five adjacent cameras. Such an array makes it possible to make a disparity map with a higher precision than a stereo camera, however it preserves the advantages of a stereo camera such as a possibility to operate in wide range of distances and in highly illuminated areas. In an outdoor environment, the array is a competitive alternative to other 3D imaging equipment such as Structured-light 3D scanners or Light Detection and Ranging (LIDAR). The considered kinds of arrays are called Equal Baseline Camera Array (EBCA). This paper presents a novel approach to calibrating the array based on the use of self-calibration methods. This paper also introduces a testbed which makes it possible to develop new algorithms for obtaining 3D data from images taken by the array. The testbed was released under open-source. Moreover, this paper shows new results of using these arrays with different stereo matching algorithms including an algorithm based on a convolutional neural network and deep learning technology.

Automated summary

This paper introduces a research on 3D scanning using a camera array called Equal Baseline Camera Array (EBCA), which can create a disparity map with higher precision than a stereo camera. By calibrating the array with self-calibration methods and developing new algorithms on an open-source testbed, 3D data can be obtained from images taken by the array. It also presents new results of using these arrays with different stereo matching algorithms, including ones based on convolutional neural networks and deep learning technology.