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

2025-02-02

AI Summary

In VR environments, users face significant challenges in locating and comprehending small, occluded features within large, homogeneous, densely packed object clusters. Method: This paper proposes a progressive refinement-based multi-scale navigation technique supporting focused hierarchical inspection. It is the first to introduce progressive refinement into VR multi-scale navigation, integrating unstructured navigation strategies with selective rendering mechanisms. A two-factor user study (navigation style × display mode) systematically evaluates performance and cognitive outcomes. Results: Unstructured navigation achieves 19% faster task completion than structured navigation; selective rendering reduces rendering latency by 23% compared to full rendering. Conversely, full rendering significantly improves spatial awareness (p < 0.01) and object understanding accuracy (+31%). The study reveals a fundamental trade-off between interaction efficiency and spatial cognition, establishing a novel design paradigm for interactive navigation in VR dense scenes.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Computer Vision: Scene Analysis & Understanding
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Virtual Reality
• Object Localization
• Dense Object Clustering

Innovation

• Virtual Reality
• Efficient Object Localization
• Progressive Refinement