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SparseLeap: Efficient Empty Space Skipping for Large-Scale Volume Rendering.

Markus Hadwiger, Ali K Al-Awami, Johanna Beyer

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|September 4, 2017

Summary

This summary is machine-generated.

SparseLeap efficiently skips empty space in large, high-resolution volume data, even with fine structures. This method optimizes rendering by pre-calculating non-empty ray segments, improving performance over traditional octree approaches.

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Area of Science:

Computer Graphics
Scientific Visualization
High-Performance Computing

Background:

Large, high-resolution volume data present rendering challenges.
Efficiently skipping empty space is crucial for performance.
Fine structures and dynamic data change complicate empty space skipping.

Purpose of the Study:

Introduce SparseLeap for efficient empty space skipping in large volumes.
Address challenges of fragmented space and frequently changing data classifications.
Improve volume rendering performance for complex datasets.

Main Methods:

Hybrid strategy balancing rasterization and ray-casting stages.
GPU hardware rasterization to create per-pixel ray segment lists.
View-independent bounding boxes and frame coherence for efficiency.

Main Results:

SparseLeap effectively skips empty space around fine structures.
Major cost of empty space skipping is moved out of the ray-casting stage.
Scales better to large, sparse data than standard octree methods.

Conclusions:

SparseLeap offers a novel and efficient solution for empty space skipping.
The hybrid approach optimizes rendering of complex volume data.
Enables faster and more scalable volume rendering for large datasets.