This paper proposes new system that generates 3D models of cattle from their multiple depth-maps for estimating their BCS (body condition scores). Various works of the agriculture are almost tedious and the use of advanced ICT is possible to improve such works. Currently, the authors have been studying such an ICT agriculture research whose targets are beef cattle. The goal of this study is to capture 3D shape information of cattle accurately for the estimation of their BCS. BCS are important data for checking whether cattle grow appropriately. However, it is very difficult to capture such information even using a commercial 3D scanner because cattle are animals and always moving. Then, the authors propose the use of multiple depth-maps of a cow simultaneously captured by multiple Kinect sensors at a different viewpoint to generate its 3D model. The problems in this case are the calibration of Kinect sensors and the synchronization of their depth-maps capturing. This paper describes how the authors solve these problems, and it shows several results of actually obtained 3D models of cattle using the proposed system.