Three-dimensional (3D) object reconstruction is to represent objects in the virtual space. It allows viewers to observe the objects at arbitrary viewpoints and feel a realistic sense. Currently, RGBD camera from Microsoft was released at a reasonable price and it has been exploited for the purpose in various fields such as education, culture, and art. In this paper, we propose a 3D object reconstruction method using multiple Kinect cameras. First, we acquire raw color and depth images from triple Kinect cameras; the cameras are placed in front of the object as a convergent form. Since raw depth images include hole regions where don't have any depth value, we employ a depth-weighted joint bilateral filter using depth differences between center and neighbor pixels in the filter kernel to fill such hole regions. In addition to that, a color mismatch problem occurs in color images from multi-view data. We exploit a color correction method by means of 3D multi-view geometry to adjust color tones in each image. After matching the correspondences between source and target images by using 3D image warping, we obtain the color correction matrix for the target image via a polynomial regression. In order to evaluate the proposed depth refinement method, we estimate the bad pixel rate (BPR) of depth images. Our results show that the BPR of the refined depth image is lower than that of the raw depth image. Through the test results, we found that the reconstructed 3D object by the proposed method is more natural than the 3D object using raw images in terms of color and shape.