A Gestalt rules and graph-cut-based simplification framework for urban building models

• An optimization framework for clustering and generalizing large-scale urban building footprints and facade textures is presented.
• The building footprints are partitioned into potential Gestalt groups.
• The graph-cut-based optimization function is employed to obtain a consistent segmentation of the buildings.
• An effective data structure is introduced to manage the aggregated building footprints and facade textures.

To visualize large urban models efficiently, this paper presents a framework for generalizing urban building footprints and facade textures by using multiple Gestalt rules and a graph-cut-based energy function. First, an urban scene is divided into different blocks by main road networks. In each block, the building footprints are partitioned into potential Gestalt groups. A footprint may satisfy several Gestalt principles. We employ the graph-cut-based optimization function to obtain a consistent segmentation of the buildings into optimal Gestalt groups with minimal energy. The building footprints in each Gestalt group are aggregated into different levels of detail (LODs). Building facade textures are also abstracted and simplified into multiple LODs using the same approach as the building footprint simplification. An effective data structure termed SceneTree is introduced to manage these aggregated building footprints and facade textures. Combined with the parallelization scheme, the rendering efficiency of large-scale urban buildings is improved. Compared with other methods, our presented method can efficiently visualize large urban models and maintain the city's image.