VGGT-MPR: VGGT-Enhanced Multimodal Place Recognition in Autonomous Driving Environments

TL;DR

VGGT-MPR introduces a unified transformer-based framework for multimodal place recognition in autonomous driving, improving robustness and accuracy through geometric-aware features and a novel re-ranking method.

Contribution

The paper presents VGGT-MPR, a novel multimodal place recognition framework using a transformer for geometric feature extraction and a training-free re-ranking mechanism, advancing robustness and efficiency.

Findings

Achieves state-of-the-art performance on large-scale benchmarks.

Demonstrates robustness to environmental changes and occlusions.

Outperforms existing methods in accuracy and computational efficiency.

Abstract

In autonomous driving, robust place recognition is critical for global localization and loop closure detection. While inter-modality fusion of camera and LiDAR data in multimodal place recognition (MPR) has shown promise in overcoming the limitations of unimodal counterparts, existing MPR methods basically attend to hand-crafted fusion strategies and heavily parameterized backbones that require costly retraining. To address this, we propose VGGT-MPR, a multimodal place recognition framework that adopts the Visual Geometry Grounded Transformer (VGGT) as a unified geometric engine for both global retrieval and re-ranking. In the global retrieval stage, VGGT extracts geometrically-rich visual embeddings through prior depth-aware and point map supervision, and densifies sparse LiDAR point clouds with predicted depth maps to improve structural representation. This enhances the discriminative ability of fused multimodal features and produces global descriptors for fast retrieval. Beyond global retrieval, we design a training-free re-ranking mechanism that exploits VGGT's cross-view keypoint-tracking capability. By combining mask-guided keypoint extraction with confidence-aware correspondence scoring, our proposed re-ranking mechanism effectively refines retrieval results without additional parameter optimization. Extensive experiments on large-scale autonomous driving benchmarks and our self-collected data demonstrate that VGGT-MPR achieves state-of-the-art performance, exhibiting strong robustness to severe environmental changes, viewpoint shifts, and occlusions. Our code and data will be made publicly available.