Abstract
We present SpatialMem, a memory-centric system for long-horizon, language-grounded retrieval and QA from egocentric video, where metric 3D serves as an interpretable indexing scaffold rather than an explicit mapping objective. Starting from casually captured egocentric RGB video, SpatialMem builds a metric-aligned spatial scaffold for indoor scenes, detects structural 3D anchors (walls, doors, windows) as first-layer support, and populates a hierarchical memory with open-vocabulary object nodes that link evidence patches, visual embeddings, and two-layer textual descriptions to 3D coordinates for compact storage and fast retrieval. This design enables interpretable, spatially grounded queries over relations (e.g., distance, direction, visibility) and supports downstream tasks such as language-guided retrieval/QA and offline navigation-style guidance over a prebuilt memory, without specialized sensors. Experiments on one public Replica scene and two real-world egocentric indoor scenes s