SCI: SGER: Application Directed Surface Parameterization

这个SGER建立在以前nsf资助的工作基础上，在光线追踪、矩阵乘法、辐射和辐射传热、地下散射、辐射传递、聚类、场景图形处理和动态自适应表面采样等领域为图形处理器（gpu）开发新算法。这些领域的进展使PI能够提出目标导向参数化的重点探索，并研究基于新指标将网格表面映射到平面域的优化方法。具体而言，提出了扩展当前对动态多分辨率网格图谱的支持的工作，以包括在交互式模型上下文中处理移动表面域的能力，而不会丢失细节。这项工作与前面提到的开发应用程序驱动的度量标准的工作是平衡的。

This SGER builds off previous NSF-funded work in developing new algorithms for graphics processors (GPUs) in areas such as ray tracing, matrix multiplication, radiosity and radiative heat transfer, subsurface scattering, radiance transfer, clustering, scene graph processing, and dynamic adaptive surface sampling. Advances in these areas allow the PI to propose focused exploration in goal-directed parameterization and investigate optimization methods for mapping meshed surfaces to a planar domain based on new metrics. Specifically, work is proposed to extend the current support for dynamic multiresolution meshed atlas to include capability to deal with moving surface domains in an interactive model context with no loss of detail. That work is balanced with the aforementioned work in developing application-driven metrics.