SciceVPR: Stable Cross-Image Correlation Enhanced Model for Visual Place Recognition

• URL: http://arxiv.org/abs/2502.20676v1
• Date: Fri, 28 Feb 2025 03:05:30 GMT
• Title: SciceVPR: Stable Cross-Image Correlation Enhanced Model for Visual Place Recognition
• Authors: Shanshan Wan, Yingmei Wei, Lai Kang, Tianrui Shen, Haixuan Wang, Yee-Hong Yang,
• Abstract summary: Visual Place Recognition (VPR) is a major challenge for robotics and autonomous systems.<n>This paper proposes stable cross-image correlation enhanced model for VPR called SciceVPR.
• Score: 4.540127373592404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Place Recognition (VPR) is a major challenge for robotics and autonomous systems, with the goal of predicting the location of an image based solely on its visual features. State-of-the-art (SOTA) models extract global descriptors using the powerful foundation model DINOv2 as backbone. These models either explore the cross-image correlation or propose a time-consuming two-stage re-ranking strategy to achieve better performance. However, existing works only utilize the final output of DINOv2, and the current cross-image correlation causes unstable retrieval results. To produce both discriminative and constant global descriptors, this paper proposes stable cross-image correlation enhanced model for VPR called SciceVPR. This model explores the full potential of DINOv2 in providing useful feature representations that implicitly encode valuable contextual knowledge. Specifically, SciceVPR first uses a multi-layer feature fusion module to capture increasingly detailed task-relevant channel and spatial information from the multi-layer output of DINOv2. Secondly, SciceVPR considers the invariant correlation between images within a batch as valuable knowledge to be distilled into the proposed self-enhanced encoder. In this way, SciceVPR can acquire fairly robust global features regardless of domain shifts (e.g., changes in illumination, weather and viewpoint between pictures taken in the same place). Experimental results demonstrate that the base variant, SciceVPR-B, outperforms SOTA one-stage methods with single input on multiple datasets with varying domain conditions. The large variant, SciceVPR-L, performs on par with SOTA two-stage models, scoring over 3% higher in Recall@1 compared to existing models on the challenging Tokyo24/7 dataset. Our code will be released at https://github.com/shuimushan/SciceVPR.

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