Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Adaptive Multi-Resolution Encoding for Interactive Large-Scale Volume Visualization through Functional Approximation

Jianxin Sun, David Lenz, Hongfeng Yu, Tom Peterka

TL;DR

A novel functional approximation multi-resolution representation, Adaptive-FAM, which is lightweight and fast to query, and a GPU-accelerated out-of-core multi-resolution volume visualization framework that directly utilizes the Adaptive-FAM representation to generate high-quality rendering with interactive responsiveness.

Abstract

Functional approximation as a high-order continuous representation provides a more accurate value and gradient query compared to the traditional discrete volume representation. Volume visualization directly rendered from functional approximation generates high-quality rendering results without high-order artifacts caused by trilinear interpolations. However, querying an encoded functional approximation is computationally expensive, especially when the input dataset is large, making functional approximation impractical for interactive visualization. In this paper, we proposed a novel functional approximation multi-resolution representation, Adaptive-FAM, which is lightweight and fast to query. We also design a GPU-accelerated out-of-core multi-resolution volume visualization framework that directly utilizes the Adaptive-FAM representation to generate high-quality rendering with interactive responsiveness. Our method can not only dramatically decrease the caching time, one of the main contributors to input latency, but also effectively improve the cache hit rate through prefetching. Our approach significantly outperforms the traditional function approximation method in terms of input latency while maintaining comparable rendering quality.

Adaptive Multi-Resolution Encoding for Interactive Large-Scale Volume Visualization through Functional Approximation

TL;DR

A novel functional approximation multi-resolution representation, Adaptive-FAM, which is lightweight and fast to query, and a GPU-accelerated out-of-core multi-resolution volume visualization framework that directly utilizes the Adaptive-FAM representation to generate high-quality rendering with interactive responsiveness.

Abstract

Functional approximation as a high-order continuous representation provides a more accurate value and gradient query compared to the traditional discrete volume representation. Volume visualization directly rendered from functional approximation generates high-quality rendering results without high-order artifacts caused by trilinear interpolations. However, querying an encoded functional approximation is computationally expensive, especially when the input dataset is large, making functional approximation impractical for interactive visualization. In this paper, we proposed a novel functional approximation multi-resolution representation, Adaptive-FAM, which is lightweight and fast to query. We also design a GPU-accelerated out-of-core multi-resolution volume visualization framework that directly utilizes the Adaptive-FAM representation to generate high-quality rendering with interactive responsiveness. Our method can not only dramatically decrease the caching time, one of the main contributors to input latency, but also effectively improve the cache hit rate through prefetching. Our approach significantly outperforms the traditional function approximation method in terms of input latency while maintaining comparable rendering quality.
Paper Structure (29 sections, 6 equations, 17 figures, 2 tables, 4 algorithms)

This paper contains 29 sections, 6 equations, 17 figures, 2 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Background and related works
  3. Out-of-core Methods
  4. Acceleration Techniques
  5. Deep Learning-based Volume Visualization
  6. Functional Approximation
  7. Advantages
  8. Rendering Quality
  9. Boundary Continuity
  10. Opportunities
  11. Method
  12. Problem Formulation and Rationale
  13. Adaptive Encoding Algorithms
  14. In-level Adaptive Encoding
  15. Cross-level Adaptive Encoding
  16. ...and 14 more sections

Figures (17)

  • Figure 1: Volume visualization using functional approximation and trilinear interpolation with and without ghost area for parallel rendering in volume space. Input is a Marschner-Lobb dataset with a resolution of $61\times61\times61$. The volume is evenly partitioned into 8 octant blocks.
  • Figure 2: Volume partition with ghost area.
  • Figure 3: An illustration of micro-blocks at 4 LODs used for rendering from a particular POV. Micro-blocks at Level 1 exhibit the highest LOD, whereas those at Level 4 display the lowest LOD.
  • Figure 4: Time breakdown of key processes for generating frames.
  • Figure 5: Micro-model storage layout. Knot and Control Point determine the shape and properties of the spline.
  • ...and 12 more figures