3D object recognition and its pose estimation for a single view query image is useful in the applications such as robot vision, place recognition, medical image analysis and manufacture automation. One straightforward method is to consider all possible view images of objects. Even though the number of view images can be reduced further, still this method requires a huge amount of storage space and computational time. Hence, most of previous works on 3D object recognition and its pose estimation were based on high-end desktop or server platform. However, with common deployment of mobile devices, object recognition and its pose estimation from captured image need to be done in a near real time by the mobile devices. In this paper, we propose a scheme for 3D object recognition and pose estimation on a mobile platform. For object recognition, we utilize the bisymmetric shape property of most objects. For pose estimation, we use SIFT (Scale Invariant Feature Transform) for candidate view images which have same shape features. Also, in order to support such functionality in a mobile platform, we propose a client-server collaboration scheme where the task can be done in a load-balanced way without sacrificing matching accuracy. Through various experiments, we show that our scheme can achieve excellent performance.