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Exploring Multiscale Navigation of Homogeneous and Dense Objects with Progressive Refinement in Virtual Reality

Leonardo Pavanatto, Alexander Giovannelli, Brian Giera, Peer-Timo Bremer, Haichao Miao, Doug Bowman

TL;DR

This work introduces PRIMO, a progressive refinement approach for multiscale VR navigation to inspect dense, homogeneous objects. By comparing structured versus unstructured navigation and Selection versus Everything display modes, it shows that unstructured navigation speeds up inspection while displaying only the focused subvolume can reduce clutter; however, showing the whole object can enhance location awareness and overall understanding. The study provides empirical and qualitative evidence guiding a hybrid design that prioritizes speed and spatial context, with practical implications for defect inspection in digital twins and similar dense-volume scenarios. This work advances VR inspection methodologies by articulating trade-offs between navigation style and display mode, informing future tool development for scalable, reliable defect analysis in complex internal geometries.

Abstract

Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are not focused on handling dense, homogeneous objects with hidden features. We propose a novel approach that applies the concept of progressive refinement to VR navigation, enabling focused inspections. We conducted a user study where we varied two independent variables in our design, navigation style (STRUCTURED vs. UNSTRUCTURED) and display mode (SELECTION vs. EVERYTHING), to better understand their effects on efficiency and awareness during multiscale navigation. Our results showed that unstructured navigation can be faster than structured and that displaying only the selection can be faster than displaying the entire object. However, using an everything display mode can support better location awareness and object understanding.

Exploring Multiscale Navigation of Homogeneous and Dense Objects with Progressive Refinement in Virtual Reality

TL;DR

This work introduces PRIMO, a progressive refinement approach for multiscale VR navigation to inspect dense, homogeneous objects. By comparing structured versus unstructured navigation and Selection versus Everything display modes, it shows that unstructured navigation speeds up inspection while displaying only the focused subvolume can reduce clutter; however, showing the whole object can enhance location awareness and overall understanding. The study provides empirical and qualitative evidence guiding a hybrid design that prioritizes speed and spatial context, with practical implications for defect inspection in digital twins and similar dense-volume scenarios. This work advances VR inspection methodologies by articulating trade-offs between navigation style and display mode, informing future tool development for scalable, reliable defect analysis in complex internal geometries.

Abstract

Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are not focused on handling dense, homogeneous objects with hidden features. We propose a novel approach that applies the concept of progressive refinement to VR navigation, enabling focused inspections. We conducted a user study where we varied two independent variables in our design, navigation style (STRUCTURED vs. UNSTRUCTURED) and display mode (SELECTION vs. EVERYTHING), to better understand their effects on efficiency and awareness during multiscale navigation. Our results showed that unstructured navigation can be faster than structured and that displaying only the selection can be faster than displaying the entire object. However, using an everything display mode can support better location awareness and object understanding.

Paper Structure

This paper contains 39 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Navigating Across Levels of Scale
  4. Perception of Multiscale Navigation
  5. PRIMO Approach
  6. Supporting Two Types of Navigation
  7. Clipping Plane
  8. Multiscale Navigation Style
  9. Display Mode
  10. Scaling and Animation
  11. Colors at Top Level
  12. User Study
  13. Experimental Design
  14. Hypotheses
  15. Experimental Task
  16. ...and 24 more sections

Figures (4)

  • Figure 1: Conditions in this study: (a) Selection-Structured (subdivided showing only focus area), (b) Selection-Unstructured (freeform showing only focus area), (c) Everything-Structured (subdivided showing entire object), and (d) Everything-Unstructured (freeform showing entire object).
  • Figure 2: Questions asked inside VR: Question 1 was asked during the task and measured location awareness; question 2 was measured at the end of an object (four trials) and measured object understanding.
  • Figure 3: Steps in this study: (a) Object being inspected, with white outline showing the focus area, and yellow rods showing the location of region to inspect; (b) clipping plane cutting over the sphere at the target location; (c) navigating down into the object (multiple steps); and (d) reaching lowest level and seeing a number representing a defect.
  • Figure 4: Time to complete: (1) clipping subtask, before starting the navigation; (2) navigation subtask, where our variables actually changed; and (3) the total task considering both. Error bars represent 95% confidence intervals.