VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change (2005.08135v2)

Abstract: Visual Place Recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance conditions and viewpoint changes and with computational constraints. VPR is related to the concepts of localisation, loop closure, image retrieval and is a critical component of many autonomous navigation systems ranging from autonomous vehicles to drones and computer vision systems. While the concept of place recognition has been around for many years, VPR research has grown rapidly as a field over the past decade due to improving camera hardware and its potential for deep learning-based techniques, and has become a widely studied topic in both the computer vision and robotics communities. This growth however has led to fragmentation and a lack of standardisation in the field, especially concerning performance evaluation. Moreover, the notion of viewpoint and illumination invariance of VPR techniques has largely been assessed qualitatively and hence ambiguously in the past. In this paper, we address these gaps through a new comprehensive open-source framework for assessing the performance of VPR techniques, dubbed "VPR-Bench". VPR-Bench (Open-sourced at: https://github.com/MubarizZaffar/VPR-Bench) introduces two much-needed capabilities for VPR researchers: firstly, it contains a benchmark of 12 fully-integrated datasets and 10 VPR techniques, and secondly, it integrates a comprehensive variation-quantified dataset for quantifying viewpoint and illumination invariance. We apply and analyse popular evaluation metrics for VPR from both the computer vision and robotics communities, and discuss how these different metrics complement and/or replace each other, depending upon the underlying applications and system requirements.

• The paper presents VPR-Bench, a framework that integrates 12 datasets and 10 VPR techniques to standardize system evaluations.
• The paper employs multiple metrics such as AUC-PR, RecallRate@N, and computational timings to measure both precision and efficiency.
• The paper quantifies viewpoint and illumination invariance, revealing performance variances between CPU and GPU implementations for real-world applications.

An Evaluation Framework for Visual Place Recognition: VPR-Bench

The paper provides a comprehensive framework for evaluating Visual Place Recognition (VPR) systems, addressing a significant gap in the standardization within this research area. The authors introduce VPR-Bench, an open-source evaluation framework designed to assess the performance of various VPR techniques using a range of datasets and evaluation metrics. VPR is pivotal in robotics and computer vision for tasks such as autonomous navigation and image retrieval, and VPR-Bench seeks to standardize performance assessments within and across these domains.

Key Contributions

The authors delineate several contributions that VPR-Bench offers to the field:

1. Integrated Datasets and Techniques: VPR-Bench incorporates 12 different datasets and a suite of 10 state-of-the-art VPR techniques. These datasets encompass a variety of environmental conditions, both indoor and outdoor, and include different levels of viewpoint and illumination variations. The techniques span handcrafted to deep learning-based approaches, providing a wide spectrum for evaluation.
2. Evaluation Metrics: The framework employs multiple evaluation strategies, including AUC-PR, RecallRate@N, ROC curves, and computational metrics like encoding and retrieval times. This comprehensive set of metrics captures both the precision and computational efficiency of VPR techniques, accommodating the diverse requirements across applications.
3. Quantified Invariance Analysis: A notable feature of VPR-Bench is its ability to quantify viewpoint and illumination invariance using the Point Features dataset. This adds a layer of analytical depth by visualizing how VPR techniques handle explicit variations in these parameters, complementing traditional performance metrics.

Analysis of Findings

The empirical analysis yields significant insights. It illustrates that no single VPR technique universally outperforms others across all datasets and metrics. Techniques like DenseVLAD demonstrate robustness in handling large-scale datasets with complex viewpoint variations, while handcrafted features such as HOG perform better in constrained environments such as the indoor Living Room dataset.

The framework also reveals discrepancies between CPU and GPU performance, highlighting computational efficiency considerations that are critical when deploying VPR systems in real-world scenarios. Moreover, the paper's analysis on the effects of adjustable ground-truth variations underscores the nuanced ways in which dataset configurations impact reported performance metrics. This points to a need for careful consideration of ground-truth specifications in future evaluations.

Implications and Future Directions

VPR-Bench serves as a vital tool for the community, encouraging transparency and replicability in VPR evaluations. By encompassing diverse datasets and metrics, it enables a more holistic comparison of VPR techniques, accommodating diverse application needs, from robotics to large-scale image retrieval tasks.

The framework's robust design offers potential extensions, such as integrating more VPR techniques and datasets, particularly those addressing extreme conditions and non-conventional environments. Additionally, the quantifiable analysis of invariance provides an innovative angle that may prompt further research into adaptive VPR techniques capable of customized trade-offs between viewpoint variance and invariance.

Overall, VPR-Bench marks a significant step toward unifying the fragmented landscape of VPR performance evaluation, offering a solid foundation for future research and development in this rapidly evolving field.