Binned <i>k</i>-d Tree Construction for Sparse Volume Data on Multi-Core and GPU Systems

Zellmann, Stefan; Schulze, Jrgen P.; Lang, Ulrich

doi:10.1109/tvcg.2019.2938957

Cited by 15 publications

(17 citation statements)

References 31 publications

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“…We can find many approaches based on a tree structure that are used to address out-of-core data (KD-tree [29,35], binary space partitioning (BSP) tree [34] or octree [12,14,17]) by linking the data through the nodes of the tree. However, Hadwiger et al [20] presented a new virtual memory approach to address several petabytes of biomedical data.…”

Section: Related Workmentioning

confidence: 99%

Interactive Visualization and On-Demand Processing of Large Volume Data: A Fully GPU-Based Out-of-Core Approach

Sarton

Courilleau

Rémion

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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In a wide range of scientific fields, 3D datasets production capabilities have widely evolved in recent years, especially with the rapid increase in their sizes. As a result, many large-scale applications, including visualization or processing, have become challenging to address. A solution to this issue lies in providing out-of-core algorithms specifically designed to handle datasets significantly larger than memory. In this article, we present a new approach that extends the broad interactive addressing principles already established in the field of out-of-core volume rendering on GPUs to allow on-demand processing during the visualization stage. We propose a pipeline designed to manage data as regular 3D grids regardless of the underlying application. It relies on a caching approach with a virtual memory addressing system coupled to an efficient parallel management on GPU to provide efficient access to data in interactive time. It allows any visualization or processing application to leverage the flexibility of its structure by managing multi-modality datasets. Furthermore, we show that our system delivers good performance on a single standard PC with low memory budget on the GPU.

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Section: Related Workmentioning

confidence: 99%

Interactive Visualization and On-Demand Processing of Large Volume Data: A Fully GPU-Based Out-of-Core Approach

Sarton

Courilleau

Rémion

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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“…We used Visionaray and its various library subsystems such as the vector math system or SIMD library component to implement several algorithms, e.g. the ones from [ZSL18], [ZHL19], [ZSL19], and [ZML19].…”

Section: Related Workmentioning

confidence: 99%

Adding Custom Intersectors to the C++ Ray Tracing Template Library Visionaray

Zellmann¹

2020

Preprint

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Figure 1: Use cases where ray tracing algorithms were extended using custom intersectors. From left to right: billboards, the alpha mask stored in the billboard images is ignored. Second from left: the alpha mask from the billboard images is used to conditionally continue BVH traversal when the surface has zero opacity near the hit point. Second from right: procedural alpha mask applied using custom intersectors. Right: debug image, the number of BVH nodes and the number of primitives inside the encountered leaf nodes is used to generate a heat map. This is done by intercepting the BVH traversal routine with a custom intersector that counts the number of ray object interactions. AbstractMost ray tracing libraries allow the user to provide custom functionality that is executed when a potential ray surface interaction was encountered to determine if the interaction was valid or traversal should be continued. This is e.g. useful for alpha mask validation and allows the user to reuse existing ray object intersection routines rather than reimplementing them. Augmenting ray traversal with custom intersection logic requires some kind of callback mechanism that injects user code into existing library routines. With template libraries, this injection can happen statically since the user compiles the binary code herself. We present an implementation of this "custom intersector" approach and its integration into the C++ ray tracing template library Visionaray.

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“…Like in [ZSL19], the GPU construction algorithm uses binning, while the CPU construction algorithm does not. We set the number of bins to 4.…”

Section: Parametersmentioning

confidence: 99%

“…In the field of real-time ray tracing with surface geometry, the LBVH data structure is generally known for its inferior culling properties but superior construction performance. The data structures from [ZSL18] and [ZSL19] especially employ very intricate construction schemes that use a cost function comparable to the surface area heuristic (SAH) [Wal07] known from surface ray tracing. In that field, it is generally accepted that the quality obtained with SAH is usually higher than that of LBVH, which uses the middle split heuristic.…”

Section: Introductionmentioning

confidence: 99%

Comparing Hierarchical Data Structures for Sparse Volume Rendering with Empty Space Skipping

Zellmann¹

2019

Preprint

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Empty space skipping can be efficiently implemented with hierarchical data structures such as k-d trees and bounding volume hierarchies. This paper compares several recently published hierarchical data structures with regard to construction and rendering performance. The papers that form our prior work have primarily focused on interactively building the data structures and only showed that rendering performance is superior to using simple acceleration data structures such as uniform grids with macro cells. In the area of surface ray tracing, there exists a trade-off between construction and rendering performance of hierarchical data structures. In this paper we present performance comparisons for several empty space skipping data structures in order to determine if such a trade-off also exists for volume rendering with uniform data topologies.

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Binned k-d Tree Construction for Sparse Volume Data on Multi-Core and GPU Systems

Cited by 15 publications

References 31 publications

Interactive Visualization and On-Demand Processing of Large Volume Data: A Fully GPU-Based Out-of-Core Approach

Interactive Visualization and On-Demand Processing of Large Volume Data: A Fully GPU-Based Out-of-Core Approach

Adding Custom Intersectors to the C++ Ray Tracing Template Library Visionaray

Comparing Hierarchical Data Structures for Sparse Volume Rendering with Empty Space Skipping

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