DP-LENS: A Density-Aware Polyfocal Lens with Topology-Driven Auto-Routing for Occlusion Management in Immersive 3D Analytics

2026-07-30

AI Summary

This work addresses the high cognitive load and low exploration efficiency in immersive 3D data analysis caused by data occlusion. We propose a density-aware multi-focus fisheye lens system that integrates topology-driven automatic path planning with large language model–enabled voice-based target selection. Our approach enables hands-free macro-navigation and low-cognitive-load data exploration while preserving peripheral context. It represents the first integration of density-aware geometric distortion, 3D perspective rendering, topological analysis, and voice interaction within a unified framework. User studies demonstrate that our method significantly reduces cognitive load and task completion time compared to baseline approaches, with automatic path planning further enhancing efficiency, reducing user fatigue, and increasing overall preference.

Abstract

Immersive environments, e.g., virtual reality (VR), offer a unique approach to exploring complex 3D datasets, where data is often heavily occluded and exploration incurs a high cognitive load. We propose DP-LENS, a density-aware polyfocal fisheye lens equipped with topology-driven auto-routing. While preserving peripheral context through geometric deformation and 3D perspective techniques, it enables users to explore 3D data with a lower cognitive load. To facilitate hands-free macro-navigation, we integrate a Large Language Model (LLM) to serve as a supplementary voice-based target selection tool that initiates the auto-routing algorithm. Two user studies with 34 participants investigate the potential benefits of this system. Our first study (N=18) compared the manual DP-LENS against two industry-standard baselines (i.e., World-in-Miniature and volumetric slicing) in heavily occluded 3D datasets. The results show that DP-LENS significantly reduced cognitive load, decreased completion time, and improved user preference. The second study (N=16) compared the topology-driven auto-routing system (initiated via voice commands) with a fully manual DP-LENS. The results show that the auto-routing system improved task efficiency, further reduced cognitive load, and garnered higher user preference. Furthermore, the auto-routing partially decoupled exploration efficiency from the physical dimensions of the data and mitigated physical fatigue to some extent. Based on the findings, we proposed design implications to inform the development of more spatially scalable and low-fatigue interactions for future 3D visual analytics systems.

Problem

• occlusion
• cognitive load
• immersive 3D analytics
• 3D data exploration
• virtual reality

Innovation

• density-aware visualization
• topology-driven auto-routing
• polyfocal lens
• occlusion management
• LLM-integrated navigation