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Spatial Memory for AR Glasses: Hierarchical Visual Acquisition and Source-Linked Recall

This paper presents a spatial-memory system for optical see-through AR glasses that recalls past physical encounters. It uses a dual-camera prototype with hierarchical visual acquisition, source-linked storage, and staged retrieval to selectively capture RGB views from monochrome observations. In a 131.35-second replay, the system selected six RGB views from 648 monitoring observations while running feature extraction on 97.7% of observations, demonstrating efficient selective capture. The architecture separates capture acceptance, interpretation, and question support, enabling bounded reassessment and source-dependent retention.

AI Brief

Provides a concrete, recent architecture for persistent AR spatial memory with quantitative results, useful for AR and computer vision researchers.

arXiv:2611.04494v1 Announce Type: new Abstract: We present a spatial-memory system that enables optical see-through AR glasses to recall earlier physical encounters. The dual-camera prototype combines hierarchical visual acquisition with source-linked storage and staged retrieval. Quality screen-ing, global and regional appearance novelty, and temporal support select monochrome observations for detailed RGB cap-ture. Semantic observations retain links to their source images, allowing retrieval to answer indexed historical questions direct-ly or inspect visual details omitted from scene descriptions. The architecture separates capture acceptance, interpretation out-come, and support for a particular question. This distinction motivates two core control mechanisms: bounded reassessment of familiar views when earlier processing leaves an evidence gap, and retention guided by source dependencies. In a 131.35 s egocentric replay, the implemented acquisition policy selected six RGB views from 648 monitoring observations, while feature extraction ran on 97.7% of observations. These results demon-strate selective RGB acquisition and identify frequent local inference as a remaining cost. Exposure duration and cross-view timing further constrain the evidence preserved from se-lected encounters. The system provides a practical acquisition-to-recall architecture for persistent AR assistance, with explicit boundaries between captured evidence, indexed interpretations, and supported historical claims.
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