F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding

TL;DR

F-Hash introduces a multi-resolution hash encoding architecture that significantly accelerates the training of neural representations for time-varying volume visualization, enabling efficient and high-quality rendering of complex datasets.

Contribution

The paper presents F-Hash, a novel feature-based multi-resolution Tesseract encoding method that improves convergence speed and encoding capacity for dynamic volumetric data.

Findings

F-Hash achieves state-of-the-art training speed on various datasets.

The encoding method is agnostic to feature detection techniques.

An adaptive ray marching algorithm enhances rendering efficiency.

Abstract

Interactive time-varying volume visualization is challenging due to its complex spatiotemporal features and sheer size of the dataset. Recent works transform the original discrete time-varying volumetric data into continuous Implicit Neural Representations (INR) to address the issues of compression, rendering, and super-resolution in both spatial and temporal domains. However, training the INR takes a long time to converge, especially when handling large-scale time-varying volumetric datasets. In this work, we proposed F-Hash, a novel feature-based multi-resolution Tesseract encoding architecture to greatly enhance the convergence speed compared with existing input encoding methods for modeling time-varying volumetric data. The proposed design incorporates multi-level collision-free hash functions that map dynamic 4D multi-resolution embedding grids without bucket waste, achieving high encoding capacity with compact encoding parameters. Our encoding method is agnostic to time-varying feature detection methods, making it a unified encoding solution for feature tracking and evolution visualization. Experiments show the F-Hash achieves state-of-the-art convergence speed in training various time-varying volumetric datasets for diverse features. We also proposed an adaptive ray marching algorithm to optimize the sample streaming for faster rendering of the time-varying neural representation.