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GPU-accelerated generation and rendering of multi-level voxel representations of solid models

机译:GPU加速生成和渲染实体模型的多层体素表示

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Solid models traditionally use boundary-representation (B-rep) to define and model their geometry. However, performing modeling operations such as Boolean operations or computing point membership classification with B-rep is computationally intensive, since B-reps do not have volumetric information. Voxelized representations, on the other hand, can be extended to include volumetric information of solid models. However, in order to use voxelized representations for solid modeling, efficient methods for voxelizing a B-rep solid model needs to be developed. In this paper, we present GPU-accelerated methods for creating and rendering a multi-level voxelization of a solid model that can be used for modeling operations. We describe two GPU-accelerated algorithms; one for creating a multi-level voxelization given a B-rep of a solid model and another for ray casting to render the multi-level voxelization of the solid model. We describe compact and flat data structures that can be used to store the multi-level voxelization data and can be efficiently retrieved in parallel by GPU-algorithms for rendering and modeling operations. The GPU-accelerated multi-level voxelization method can generate models with an effective voxel count of up to 8 billion voxels. In addition, the GPU voxelization algorithm is more than 40x faster than the CPU implementation in generating the voxelization. Finally, we outline a few applications for the voxel representation, which include fast point-membership classification, volume computation, and collision detection. (C) 2018 Elsevier Ltd. All rights reserved.
机译:传统上,实体模型使用边界表示(B-rep)定义和建模其几何形状。但是,由于B-rep没有体积信息,因此执行建模操作(例如布尔操作或使用B-rep进行计算点隶属度分类)在计算上会很费力。另一方面,体素化表示可以扩展为包括实体模型的体积信息。但是,为了将体素化表示形式用于实体建模,需要开发一种有效体素化B-rep实体模型的方法。在本文中,我们介绍了GPU加速的方法,用于创建和渲染可用于建模操作的实体模型的多级体素化。我们描述了两种GPU加速算法;一个用于在给定实体模型的B-rep的情况下创建多级体素化,另一个用于射线投射以渲染实体模型的多级体素化。我们描述了紧凑而平坦的数据结构,这些数据结构可用于存储多级体素化数据,并且可以通过GPU算法并行高效地进行检索,以进行渲染和建模操作。 GPU加速的多级体素化方法可以生成有效体素数高达80亿体素的模型。此外,GPU体素化算法在生成体素化方面比CPU实现快40倍以上。最后,我们概述了体素表示的一些应用程序,包括快速点成员资格分类,体积计算和碰撞检测。 (C)2018 Elsevier Ltd.保留所有权利。

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