Constraint-base lidar point cloud fitting

Airborne Light Detection and Ranging (LiDAR) has the ability of acquiring high-resolution and high-accuracy point clouds. The processing on point clouds has thus become an important research topic and has drawn increasing attention in the fields of remote sensing. An increasing number of 3D building models have been available in the Internet with the development of Web 2.0 techniques and scanning equipment. Many web-based data-sharing platforms, such as Google 3D Warehouse and MakerBot Thingiverse, provide functions for users to upload and share their models. Therefore, a fitting approach is proposed to construct building models using airborne LiDAR data. An iterative approach consists of three main parts, geometric analysis, point cloud segmentation, and model refinement, in proposed the experimental result shows that the proposed approach can generate 3D building models efficiently.