3D registered point cloud
We propose a camera calibration method to generate a high-quality and photorealistic 3D (dimension) volumetric graphics model using several low-cost commercial RGB-D (depth) cameras located in a limited space. We show an efficient workflow to register a model efficiently and propose iterative calibration techniques to construct it. Using multiple frames, calibration in the vertical direction between the upper and lower cameras is performed. After selecting any four pairs, the calibration is performed while rotating with the vertical calibration results from other adjacent viewpoints. After performing calibration between each camera pair, the calibration is repeated by creating a virtual viewpoint between each camera pair. The error function between 3D coordinates of feature points acquired from the RGB image is obtained, and an attempt is made to minimize this. When the error value converges below the threshold value by optimizing the error function, calibration ends, and the final extrinsic parameter is obtained as a result. After performing 3D reconstruction using the proposed calibration algorithm, a 3D point cloud is produced. Finally, a simple and efficient refinement algorithm is proposed to improve the 3D point cloud quality. We show the advantage of the proposed technique by quantitatively comparing the calibration results using two ground truth data and 3D reconstruction results using them in the experimental results. Also, the characteristics of the proposed method are expressed from various viewpoints using various visual verification methods.
This dataset is for our paper submission.