REMIND: RE-Identification with Memory for INDoor Navigation

2026-07-10 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

REMIND is an online tracker designed for long-term multi-object re-identification of generic indoor objects using monocular RGB imagery, without requiring camera pose or depth. It addresses the challenge of re-identifying objects after significant temporal gaps, viewpoint changes, and illumination variations, a limitation for current multi-object tracking, re-identification, and video object segmentation methods. REMIND integrates frozen DINOv3 features with a dual-bank multi-prototype appearance memory, part- and background-level descriptors, a neighbour-context reasoning module for spatial co-occurrence, and joint Hungarian assignment with ambiguity-aware safeguards. On a purpose-built indoor dataset, REMIND achieved 90.35% IDF1, nearly 20 points above a state-of-the-art video object segmentation baseline and over 36 points above a strong tracking-by-detection baseline. It also attained the highest IDF1 on ScanNet++ in almost all settings and completed every scene, unlike a video object segmentation baseline that exhausted GPU memory on 66.9% of scenes. The complete system, evaluation framework, and dataset are publicly available.

Key takeaway

For Robotics Engineers or Computer Vision Engineers developing indoor navigation systems requiring robust, long-term object re-identification, REMIND offers a substantial performance improvement. Its memory-based approach and spatial context reasoning achieve 90.35% IDF1, significantly outperforming video object segmentation and tracking-by-detection baselines in complex indoor settings. You should explore integrating REMIND's publicly released system or its architectural principles to enhance your robot's ability to persistently recognize objects across revisits and varying conditions.

Key insights

REMIND uses memory and spatial context with DINOv3 features for robust, long-term indoor object re-identification.

Principles

• Humans rely on accumulated appearance familiarity and spatial context.
• Persistent memory mechanisms are crucial for long-term re-identification.
• Global identity consistency improves object tracking over reactive filtering.

Method

REMIND combines frozen DINOv3 features, dual-bank multi-prototype appearance memory, part/background descriptors, neighbour-context reasoning, and joint Hungarian assignment for online multi-object re-identification.

In practice

• Deploy REMIND for robust indoor mobile robot navigation.
• Utilize DINOv3 features for appearance-based object recognition.
• Integrate spatial co-occurrence for improved object association.

Topics

• Multi-Object Re-identification
• Indoor Navigation
• Mobile Robotics
• Computer Vision
• DINOv3 Features
• Object Tracking

Best for: Research Scientist, AI Scientist, Robotics Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.