This paper proposes a method for simultaneous global registration of multiple depth images which are obtained from multiple viewpoints. Unlike the previous method, the proposed method fully utilizes a silhouette-based cost function taking out-of-view and non-overlapping regions into account as well as depth differences at overlapping areas. With the combination of the above cost functions and a recent powerful meta-heuristics named self-adaptive Differential Evolution, it realizes the entire shape reconstruction from relatively small number (three or four) of depth images, which do not involve enough overlapping regions for Iterative Closest Point even if they are prealigned. In addition, to allow the technique to be applicable not only to time-of-flight sensors, but also projector-camera systems, which has deficient silhouette by occlusions, we propose a simple solution based on color-based silhouette. Experimental results show that the proposed method can reconstruct the entire shape only from three depth images of both synthetic and real data. The influence of noises and inaccurate silhouettes is also evaluated.