空天三维仿真中空间目标实时渲染算法  

Real-time Rendering Algorithm for Spatial Targets in Air-space 3D Simulation

在线阅读下载全文

作  者:张春晖 聂芸 王国伟 ZHANG Chun-hui;NIE Yun;WANG Guo-wei(The 15th Research Institute,China Electronics Technology Group Corporation,Beijing 100083,China)

机构地区:[1]中国电子科技集团公司第十五研究所,北京100083

出  处:《计算机与现代化》2023年第11期82-88,共7页Computer and Modernization

基  金:中国电子科技集团公司第十五研究所国防预研项目(GFZX03010105280203)。

摘  要:近年来,随着载人航天研究的进一步加深,空天任务的复杂性和可靠性要求也日益提高。对海量目标的位置实时解算和场景渲染是空间目标实时渲染的重难点。利用层次细节模型(LOD)在动态渲染中的优势,本文提出一种海量的空间目标实时渲染方法,该算法侧重于把传统的批LOD模型优化成基于R树的LOD模型。在构建基于R树的LOD模型时,会出现索引空间重叠、查询效率低、LOD模型纹理突变等问题。因此,提出基于节点的深度调整策略消除索引空间重叠,采用快速剪枝算法提高查询效率,使用基于Shader的Alpha测试技术实现LOD模型平滑过渡,通过上述3种优化算法的协同处理,优化后的LOD模型在场景渲染时间、空间占有率、帧率等均有所改善。In recent years,with the deepening research of manned spaceflight,the complexity and reliability requirements of space missions have been increasing.Real-time position calculation of massive targets and scene rendering are the key and difficult points of real-time rendering of space targets.To take advantage of the hierarchical detail model(LOD)in dynamic rendering,a real-time rendering method for massive space targets is proposed,which focuses on optimizing the traditional batch LOD model into an R-tree-based LOD model.When constructing the R-tree-based LOD model,there are issues such as index space overlap,low query efficiency,and LOD model texture mutation.Therefore,a node-based depth adjustment strategy is proposed to eliminate index space overlap,a fast pruning algorithm is adopted to improve query efficiency,and the shader-based alpha testing technique is used to achieve smooth transitions between LOD models.Through the collaborative processing of these three optimization algorithms,the optimized LOD model has improved in scene rendering time,space occupancy rate,frame rate,and other aspects.

关 键 词:三维仿真 R树 LOD模型 实时渲染 

分 类 号:TP391.9[自动化与计算机技术—计算机应用技术]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象