abstract
- © 2020 SPIE.Computer vision systems are important for capturing environments, for facial recognition and as a way to scan objects for documenting and for manufacturing. One of the current challenges is to scan objects that change dynamically, whether rigid transformations or shape deformations. This paper presents a new system based on an RGB-D camera array, an array which is calibrated by means of a set of equations that relate the distance, angles and resolution of the cameras. The Iterative Closest Point algorithm is proposed for a fine alignment, as well with a process of reconstruction and elimination of noise by means of a Poisson distribution function. The system was exhaustively validated using two forms with different properties. When comparing the obtained result of the scan versus the real models by means of the distance of Hausdorff, errors of no more than 0.0045 mm were obtained. In addition, an experiment is performed by scanning the palm of the hand under deformations and movements. These results show that the system can scan static and non-static and dynamic forms, thereby demonstrating its usefulness for the reconstruction, analysis and manufacture of objects of different classes.