Recent advances in range measurement devices have opened up new opportunities and challenges for fast 3D modeling of large scale outdoor environments. Applications of such technologies include virtual walk and fly through, urban planning, disaster management, object recognition, training, and simulations. In this paper, we present general methods for surface reconstruction and segmentation of 3D colored point clouds, which are composed of partially ordered ground-based range data registered with airborne data. Our algorithms can be applied to a large class of LIDAR data acquisition systems, where ground-based data is obtained as a series of scan lines. We develop an efficient and scalable algorithm that simultaneously reconstructs surfaces and segments ground-based range data. We also propose a new algorithm for merging ground-based and airborne meshes which exploits the locality of the ground-based mesh. We demonstrate the effectiveness of our results on data sets obtained by two different acquisition systems. We report results on a ground-based point cloud containing 94 million points obtained during a 20 km drive.