One of the most critical aspects of documenting distinct sites is acquiring detailed and accurate range information. Several three-dimensional (3-D) acquisition techniques are available, but each has its own limitations. This paper presents a range data fusion method with the aim to enhance the descriptive contents of the entire 3-D reconstructed model. A kernel function is introduced for supervised classification of the range data using a kernelized support vector machine. The classification method is based on the local saliency features of the acquired range data. The range data acquired from heterogeneous range sensors are transformed into a defined common reference frame. Based on the segmentation criterion, the fusion of range data is performed by integrating finer regions of range data acquired from a laser range scanner with the coarser region of Kinect’s range data. After fusion, the Delaunay triangulation algorithm is applied to generate the highly accurate, realistic 3-D model of the scene. Finally, experimental results show the robustness of the proposed approach.