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VisiMark: Characterizing and Augmenting Landmarks for People with Low Vision in Augmented Reality to Support Indoor Navigation

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arxiv 2502.10561 v1 pith:YBOCJIEZ submitted 2025-02-14 cs.HC

classification cs.HC
keywords landmarklandmarksvisimarkpeoplevisionaugmentationsidentifyingimportant
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Landmarks are critical in navigation, supporting self-orientation and mental model development. Similar to sighted people, people with low vision (PLV) frequently look for landmarks via visual cues but face difficulties identifying some important landmarks due to vision loss. We first conducted a formative study with six PLV to characterize their challenges and strategies in landmark selection, identifying their unique landmark categories (e.g., area silhouettes, accessibility-related objects) and preferred landmark augmentations. We then designed VisiMark, an AR interface that supports landmark perception for PLV by providing both overviews of space structures and in-situ landmark augmentations. We evaluated VisiMark with 16 PLV and found that VisiMark enabled PLV to perceive landmarks they preferred but could not easily perceive before, and changed PLV's landmark selection from only visually-salient objects to cognitive landmarks that are more important and meaningful. We further derive design considerations for AR-based landmark augmentation systems for PLV.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. What to Distinguish and How? Opportunities and Challenges of Augmenting Multiple, Cluttered Objects in Complex Scenes for People with Low Vision

    cs.HC 2026-07 conditional novelty 6.0 of 10

    For people with low vision, AR overlays that rank objects by importance redirect attention toward high-priority objects, but multi-object augmentation lowers overall scene recall and creates new visual-confusion problems.

  2. Reality Proxy: Fluid Interactions with Real-World Objects in MR via Abstract Representations

    cs.HC 2025-07 conditional novelty 6.0 of 10

    Reality Proxy replaces direct selection of physical objects in MR with AI-enriched, hand-placed abstract proxies that support skimming, multi-selection, filtering, and semantic grouping.

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