Abstract
Progressive encoding and transmission, i.e., a crucial technical foundation of 3D Web Geographic Information Systems (WebGIS), addresses the contradiction between massive 3D building data and limited network transmission capacity. Most progressive encoding algorithms, taking vertices, edges or triangles as encoding units, may break the inherent geometric and topological characteristics of 3D building models. Thus, a novel 3D building model encoding method that can maintain the internal characteristics is proposed, which can be used for high-efficiency progressive transmission. With this method, each building is decomposed into three types of fundamental structures: main structure, independent structure and attached structure. A structural topology graph (STG) was constructed based on the connections among structures. Guided by STG, one or more structures were wrapped as the smallest incremental transmission unit, denoted as transmission node. When requested, the real-time position of viewpoint, orientation and visual importance of nodes are used to pick up expected nodes for responding. The results confirm that the proposed method can better maintain the geometric and topological characteristics while encoding 3D building models. While serving for transmission, the proposed method not only effectively reduces the transmission load, but also provides users with a better consistency experience on the building appearance at different simplification levels.
Highlights
The 3D building model, which is widely used in 3D navigation and digital cities, is essential to the virtual geographic environment [1]. 3D building models have substantial data because of their rich details
This study explores a novel 3D building model encoding method that can maintain
Structure, creates the the transmission node and stores the transmission node dataextracts to a disk
Summary
The 3D building model, which is widely used in 3D navigation and digital cities, is essential to the virtual geographic environment [1]. 3D building models have substantial data because of their rich details. 3D building models have substantial data because of their rich details. Massive building model data impose a heavy load on network transmission and pose a huge challenge to client rendering [2]. Because no correlations exist among the models at different levels, a lot of data is repeatedly transmitted, greatly wasting network transmission resources. To address the contradiction between massive model data and limited network transmission capacity, a progressive encoding and transmission strategy has been developed [4,5,6,7]. The basic model is first transmitted, and increments are transmitted progressively to refine the model on demand or in a fixed order, which significantly reduces data redundancy among a series of transmissions [8]
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