矢田 紀子

Recent advances in depth measurement and its utilization have made point cloud processing more critical. Additionally, the human head is essential for communication, and its three-dimensional data are expected to be utilized in this regard. However, a single RGB-Depth (RGBD) camera is prone to occlusion and depth measurement failure for dark hair colors such as black hair. Recently, point cloud completion, where an entire point cloud is estimated and generated from a partial point cloud, has been studied, but only the shape is learned, rather than the completion of colored point clouds. Thus, this paper proposes a machine learning-based completion method for colored point clouds with XYZ location information and the International Commission on Illumination (CIE) LAB (L*a*b*) color information. The proposed method uses the color difference between point clouds based on the Chamfer Distance (CD) or Earth Mover’s Distance (EMD) of point cloud shape evaluation as a color loss. In addition, an adversarial loss to L*a*b*-Depth images rendered from the output point cloud can improve the visual quality. The experiments examined networks trained using a colored point cloud dataset created by combining two 3D datasets: hairstyles and faces. Experimental results show that using the adversarial loss with the colored point cloud renderer in the proposed method improves the image domain’s evaluation.

Copyright © 2018 by ITE Transactions on Media Technology and Applications (MTA). This paper proposes a point groups-based algorithm for point cloud registration. Most of the existing algorithms align two point clouds globally; however, they are unsuitable when the overlapping ratio is low or the inputs do not have strong features. The high accuracy of matched points is conducive for a rigid transformation of point clouds. This study aims to determine the exact matching points to register point clouds. The proposed method is based on point groups that are resampled point clouds. Subsequently, we calculate the multiple average probability (MAP) for each point group and match them by a sparse representation. Finally, the coherent point drift (CPD) algorithm is used to register the matched point groups, and the same transformation is applied to register the point clouds. The experimental results show that in terms of robustness to noise and outliers, our algorithm can register point clouds with a low overlapping ratio.

Stereoscopic video technology, which enables three-dimensional (3D) images to be displayed, has been developing rapidly. However, existing devices are unable to achieve accurate color reproduction. This paper proposes a method to accurately reproduce the colors displayed by a multiband 3D projector. Previously, we proposed a stereoscopic display system with an expanded color gamut. However, we only confirmed the expansion of the color gamut of the proposed system and were unable to display stereoscopic images with accurate colors. We now propose an accurate color and spectral reproduction method for a stereoscopic image display system for which we developed an expanded color gamut by means of the covariance matrix adaptation evolution strategy (CMA-ES). The system design for optimizing the color reproduction of the multiband 3D projector is described. An experimental evaluation of the color reproducibility showed the performance of the proposed method to be superior to that of an existing method.