SpatialMem: Metric-Aligned Long-Horizon Video Memory for Language Grounding and QA

TL;DR

SpatialMem is a novel memory system that builds a metric-aligned spatial scaffold from egocentric video, enabling interpretable, spatially grounded retrieval and question answering for indoor scenes without specialized sensors.

Contribution

The paper introduces SpatialMem, a hierarchical, metric-aligned memory system for long-horizon, language-grounded video understanding and retrieval in indoor environments.

Findings

Maintains stable layout reasoning and retrieval across cluttered scenes.

Enhances path-level grounding with two-layer description memory.

Limited degradation under moderate scale perturbation.

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 show that SpatialMem maintains stable layout reasoning, offline guidance, and hierarchical retrieval across these evaluated scenes despite increasing clutter and occlusion. A compact ablation further shows that the two-layer description memory improves path-level grounding, while moderate scale perturbation causes only limited degradation. These results position SpatialMem as an efficient and extensible memory interface for spatially grounded long-horizon video understanding.