The development of hardware and software is not sufficient to meet the real-time visualization requirements of large
scale 3D City Models. How to adaptively coordinate the speed and quality of rendering according to the data volume and
hardware/software environment is therefore a critical issue. This paper proposes an algorithm which predicts the
rendering time according to the features of 3D City Models at first, and then to calculate the object importance value
based on the mathematical model which considers the indicators of each object: location, distance, visibility and
semantics, and finally to select the object set to be rendered by a fast recursive algorithm. There are five factors selected
to test their influence on rendering time: triangle number, vertex number, texture number, screen pixel number, and the
texture image size. According to multivariate statistical theory, experimental results prove that both geometry and texture
data size are significant for rendering time of 3D City Models. A typical 3D building group models are employed for
experimental analysis. The results show that the method introduced in this paper is accurate to predict the time of
rendering 3D models with detailed texture. The adaptive rendering performance is also significantly improved.
Rendering complex building scene like ancient architecture heritage is always a difficult work since there is very high
occlusion ratio. According to the architectural structure and the data organization strategy of 3D ChiLin models, a new
occlusion culling algorithm is proposed, which is based on the normal line grouping of the models concerned with the
building features. By integrating with other accelerate methods such as frustum culling, detail culling, LOD and
classifying rendering, this algorithm can improve the rendering speed efficiently.
This paper presents a dual approach of autonomous navigation for intelligent virtual agent in large complex virtual urban environments. A hierarchical global road map of the environment for global path planning is precomputed at first by using of the constrained Delauny triangulation algorithm to partition the free space, which takes into consideration the uneven feature of terrain. The accuracy of the road map may be adjusted by resizing the length of constrained segment. At runtime, A* algorithm is performed to automatically computes a collision-free and constrained path between two specified locations, and then the local navigation is realized by obtaining the environmental information immediately through visual image based 3D virtual vision. Experimental results demonstrate that the approach can provide satisfying global path and real-time obstacle avoidance effect.