GPU-accelerated generation and rendering of multi-level voxel representations of solid models. (October 2018)

• Record Type: Journal Article
• Authors: Young, Gavin; Krishnamurthy, Adarsh
• Is Part Of: Computers & graphics. Volume 75(2018)
• Page Start: 11
• Page End: 24
• Publication Date: 2018-10
• Subjects: Multi-level voxelization -- Boundary-representation -- GPU-accelerated geometric algorithms -- Volume rendering -- Ray-casting -- Geometric modeling; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2018.07.003
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: GPU-accelerated method to create a multi-level voxelization of B-rep solid models. Flat storage data structures for efficient access using the GPU. GPU-accelerated ray casting for direct rendering of voxelization at interactive fps. Augmented voxel data structure with normals for realistic surface rendering. Graphical abstract: Abstract: 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.