Aiming at the increasing critical issues of existing 2D plans and map-based methodology for integrated management of advanced buildings and related dynamic property rights in complicated 3D built environments, a novel semantics-based 3D dynamic house property model with hierarchical levels of detail is proposed in this paper, based on comprehensive analysis of 3D house property objects and various application requirements. This model is characterized by: (1) 3D geometric semantics: a 3D geometry hierarchy of exterior and interior of buildings is defined; (2) thematic semantics, comprehensive house property object and related property right relationships are illustrated; (3) temporal semantics, dynamic representation of house property driven by both geometric events and property right events is involved. This model facilitates comprehensive data mining to analyze spatial relationships and dynamic change of property rights in real 3D built environments and can also support the sale and lease of real estate, facility management, house planning and so on.

The extraction of object features from massive unstructured point clouds with different local densities, especially in the presence of random noisy points, is not a trivial task even if that feature is a planar surface. Segmentation is the most important step in the feature extraction process. In practice, most segmentation approaches use geometrical information to segment the 3D point cloud. The features generally include the position of each point (X, Yand Z), locally estimated surface normals and residuals of best fitting surfaces; however, these features could be affected by noisy points and in consequence directly affect the segmentation results. Therefore, massive unstructured and noisy point clouds also lead to bad segmentation (over-segmentation, undersegmentation or no segmentation). While the RANSAC (random sample consensus) algorithm is effective in the presence of noise and outliers, it has two significant disadvantages, namely, its efficiency and the fact that the plane detected by RANSAC may not necessarily belong to the same object surface; that is, spurious surfaces may appear, especially in the case of parallel-gradual planar surfaces such as stairs. The innovative idea proposed in this paper is a modification for the RANSAC algorithm called Seq-NV-RANSAC. This algorithm checks the normal vector (NV) between the existing point clouds and the hypothesised RANSAC plane, which is created by three random points, under an intuitive threshold value. After extracting the first plane, this process is repeated sequentially (Seq) and automatically, until no planar surfaces can be extracted from the remaining points under the existing threshold value. This prevents the extraction of spurious surfaces, brings an improvement in quality to the computed attributes and increases the degree of automation of surface extraction. Thus the best fit is achieved for the real existing surfaces.

Aiming at the fundamental issue of optimal design of discrete levels of detail (LOD) for the visualization of complicated 3D building fac-ades, this paper presents a new quantitative analytical method of perceptible 3D details based on perceptual metric. First, the perceptual metric is defined as the quantitative indicator of the visual perceptibility of facade details at a given viewing distance. Then, according to the human visionsystem, an algorithm employing 2D discrete wavelet transform and contrast sensitivity function is developed to extract the value of perceptual metric from the rendered image of the facade. Finally, a perceptual metric function is defined, based on the perceptual metric values extracted at equal interval viewing distances. The minimum detail redundancy model is then proposed for the optimal design of discrete LODs. This method provides a quantitative instruction for generating discrete LODs. The experimental results prove the effectiveness and great potential of this method.

The existing road-network models based on the 2D link-node of roadway centrelines have inhibited lane-oriented network flow analysis and multi-dimensional inventory management in complicated 3D urban environments. This paper proposes a hierarchical lane-oriented 3D road-network model (HL-3DRNM), with a unified modelling language (UML) diagram. HL-3DRNM is a non-planar topological model with the support of a 3D lane ribbon cartographic display, which is characterized by: (1) multiple topological and cartographic representations and various abstraction levels (street, road segment, carriageway and lane); and (2) referenced multi-dimensional road information (point, line, area and volume) at lane level. HL-3DRNM provides solid mathematical foundations for a more detailed inventory management, effective network analysis and realistic navigation in the increasingly complicated 3D urban transportation systems.