StructVPR++: Distill Structural and Semantic Knowledge With Weighting Samples for Visual Place Recognition

Area of Science:

• Computer Vision
• Robotics
• Artificial Intelligence

Background:

• Visual place recognition is crucial for autonomous systems but challenging for RGB images.
• Current methods struggle to balance global feature extraction accuracy with efficiency.
• Two-stage approaches offer better accuracy but are computationally expensive.

Purpose of the Study:

• To develop an efficient and accurate visual place recognition framework for autonomous driving and robotics.
• To bridge the performance gap between global retrieval and computationally intensive re-ranking methods.
• To embed structural and semantic knowledge into global image representations.

Main Methods:

• Proposed StructVPR++ framework using segmentation-guided distillation.
• Decoupled label-specific features from global descriptors for semantic alignment.
• Introduced a sample-wise weighted distillation strategy to improve training robustness.

Main Results:

• StructVPR++ significantly improved Recall@1 by 5-23% compared to state-of-the-art global methods.
• Outperformed many two-stage visual place recognition approaches in accuracy.
• Achieved real-time efficiency using only a single RGB input.

Conclusions:

• StructVPR++ offers an effective trade-off between accuracy and efficiency in visual place recognition.
• The method enables explicit semantic alignment without requiring segmentation during deployment.
• StructVPR++ represents a significant advancement for real-time visual localization in robotics and autonomous driving.