TENSOR-BASED QUALITY PREDICTION FOR BUILDING MODEL RECONSTRUCTION FROM LIDAR DATA AND TOPOGRAPHIC MAP
- Department of Geomatics, National Cheng Kung University, 1 University Road, Tainan City, Taiwan
Keywords: LiDAR, tensor analysis, robust least squares, data fusion, building model reconstruction
Abstract. A quality prediction method is proposed to evaluate the quality of the automatic reconstruction of building models. In this study, LiDAR data and topographic maps are integrated for building model reconstruction. Hence, data registration is a critical step for data fusion. To improve the efficiency of the data fusion, a robust least squares method is applied to register boundary points extracted from LiDAR data and building outlines obtained from topographic maps. After registration, a quality indicator based on the tensor analysis of residuals is derived in order to evaluate the correctness of the automatic building model reconstruction. Finally, an actual dataset demonstrates the quality of the predictions for automatic model reconstruction. The results show that our method can achieve reliable results and save both time and expense on model reconstruction.