Unmanned ground vehicle (UGV) technology benefits for intelligent transportation and automatic digital map reconstruction in smart city domain. In most urban areas, the planes are usually existed in buildings and infrastructures, which consist of massive 3D points detected by the sensors on UGV. This paper aims to study planar detection from 3D point clouds and represent the planes using a mesh instead of plenty of points in 3D reconstruction. A fast planar detection method using GPU-based Random Sample Consensus (RANSAC) is developed to estimate the planar parameters existed in surrounding environment. The sensed 3D point clouds are segmented into several planes. Each planar surface is reconstructed and rendered using only four vertices, so that the memory usage is reduced in 3D reconstruction. For improving the time efficiency in plane recognition process, GPU is utilized to fasten RANSAC algorithm using CUDA parallel programming technology.