MARV: Multiview Augmented Reality Visualisation for Exploring Rich Material Data

TL;DR

MARV introduces an immersive augmented reality system with novel visualization techniques to enhance analysis of complex, high-dimensional material data, improving pattern recognition and temporal change detection compared to traditional methods.

Contribution

The paper presents MARV, a new AR-based visual analytics system with three innovative visualization techniques for exploring rich material data.

Findings

Enhanced pattern detection in material data

Improved temporal analysis capabilities

Positive expert feedback on visualization effectiveness

Abstract

Rich material data is complex, large and heterogeneous, integrating primary and secondary non-destructive testing data for spatial, spatio-temporal, as well as high-dimensional data analyses. Currently, materials experts mainly rely on conventional desktop-based systems using 2D visualisation techniques, which render respective analyses a time-consuming and mentally demanding challenge. MARV is a novel immersive visual analytics system, which makes analyses of such data more effective and engaging in an augmented reality setting. For this purpose, MARV includes three newly designed visualisation techniques: MDD Glyphs with a Skewness Kurtosis Mapper, Temporal Evolution Tracker, and Chrono Bins, facilitating interactive exploration and comparison of multidimensional distributions of attribute data from multiple time steps. A qualitative evaluation conducted with materials experts in a real-world case study demonstrates the benefits of the proposed visualisation techniques. This evaluation revealed that combining spatial and abstract data in an immersive environment improves their analytical capabilities and facilitates the identification of patterns, anomalies, as well as changes over time.