论文标题

Nanomatrix:拥挤的生物环境的可扩展结构

Nanomatrix: Scalable Construction of Crowded Biological Environments

论文作者

Alharbi, Ruwayda, Strnad, Ondřej, Klein, Tobias, Viola, Ivan

论文摘要

我们提出了一种新颖的方法,用于互动结构和极大的分子场景的渲染,能够用原子细​​节代表多个生物细胞。我们的方法是根据给定的一组建筑规则量身定制的,该场景是在程序上构造的。大型场景的渲染通常需要整个场景内可用的核心,或者,它需要核心外管理将数据加载到存储器层次结构中,作为渲染循环的一部分。我们建议在过程中随时生成点播场景,而不是核心内存管理。关键的想法是一个与位置和观点相关的程序场景结构策略,在任何给定时间,GPU内存中只有一小部分原子场景可用。使用覆盖整个场景的网格将原子细节填充成一个均匀的空间分区。大多数网格单元不充满几何形状,只有那些被摄像机可能看到的人群。原子细节在计算着色器中填充,其表示形式与用于现代GPU的硬件射线追踪的加速数据结构有关。从给定的角度看不到原子细节的对象,这些对象是由带有无缝纹理映射的三角形网格表示的,这些纹理是由原子细节的几何形状产生的。该算法由两个管道组成,即施工式管道和渲染管道,它们在原子分辨率上渲染分子场景,远远超出了含有数万本原子的GPU记忆的极限。我们在SARS-COV-2和红细胞的多种模型上演示了我们的技术。

We present a novel method for the interactive construction and rendering of extremely large molecular scenes, capable of representing multiple biological cells in atomistic detail. Our method is tailored for scenes, which are procedurally constructed, based on a given set of building rules. Rendering of large scenes normally requires the entire scene available in-core, or alternatively, it requires out-of-core management to load data into the memory hierarchy as a part of the rendering loop. Instead of out-of-core memory management, we propose to procedurally generate the scene on-demand on the fly. The key idea is a positional- and view-dependent procedural scene-construction strategy, where only a fraction of the atomistic scene around the camera is available in the GPU memory at any given time. The atomistic detail is populated into a uniform-space partitioning using a grid that covers the entire scene. Most of the grid cells are not filled with geometry, only those are populated that are potentially seen by the camera. The atomistic detail is populated in a compute shader and its representation is connected with acceleration data structures for hardware ray-tracing of modern GPUs. Objects which are far away, where atomistic detail is not perceivable from a given viewpoint, are represented by a triangle mesh mapped with a seamless texture, generated from the rendering of geometry from atomistic detail. The algorithm consists of two pipelines, the construction-compute pipeline, and the rendering pipeline, which work together to render molecular scenes at an atomistic resolution far beyond the limit of the GPU memory containing trillions of atoms. We demonstrate our technique on multiple models of SARS-CoV-2 and the red blood cell.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源