Close, But Not There: Boosting Geographic Distance Sensitivity in Visual Place Recognition (2407.02422v1)

Abstract: Visual Place Recognition (VPR) plays a critical role in many localization and mapping pipelines. It consists of retrieving the closest sample to a query image, in a certain embedding space, from a database of geotagged references. The image embedding is learned to effectively describe a place despite variations in visual appearance, viewpoint, and geometric changes. In this work, we formulate how limitations in the Geographic Distance Sensitivity of current VPR embeddings result in a high probability of incorrectly sorting the top-k retrievals, negatively impacting the recall. In order to address this issue in single-stage VPR, we propose a novel mining strategy, CliqueMining, that selects positive and negative examples by sampling cliques from a graph of visually similar images. Our approach boosts the sensitivity of VPR embeddings at small distance ranges, significantly improving the state of the art on relevant benchmarks. In particular, we raise recall@1 from 75% to 82% in MSLS Challenge, and from 76% to 90% in Nordland. Models and code are available at https://github.com/serizba/cliquemining.

• The paper introduces CliqueMining to enhance GDS, achieving a recall@1 boost from 76% to 90.7% on the Nordland dataset.
• It constructs a graph of visually similar frames to form challenging cliques and trains models with a Multi-Similarity loss.
• The improved GDS leads to more accurate place recognition, benefiting applications like autonomous navigation and augmented reality.

Close, But Not There: Boosting Geographic Distance Sensitivity in Visual Place Recognition

The paper "Close, But Not There: Boosting Geographic Distance Sensitivity in Visual Place Recognition" by Sergio Izquierdo and Javier Civera provides an in-depth analysis of the limitations associated with Geographic Distance Sensitivity (GDS) in current Visual Place Recognition (VPR) models. The authors propose a novel mining strategy, CliqueMining, to address these issues effectively.

Introduction

Visual Place Recognition (VPR) is pivotal in many visual localization and mapping tasks, encapsulating the retrieval of geotagged reference images that are visually similar to a given query image. This process heavily relies on the embedding of images into a space where the nearest neighbors are expected to be geographically closest to the query image. However, embedding spaces used in current VPR models often fail to correlate adequately with geographic distances, particularly at close ranges. Consequently, this results in models that struggle with ranking closely spaced locations accurately, impeding their recall performance.

Geographic Distance Sensitivity in VPR

The primary contribution of the paper is the identification and formalization of Geographic Distance Sensitivity (GDS). GDS quantifies the model's ability to assign smaller descriptor distances to image pairs that are geographically closer. The authors illustrate that contemporary VPR models exhibit a low GDS on closely spaced samples. This deficiency is underscored by two conditions: (i) the expected descriptor distance for geographically closer images should be smaller, and (ii) the dispersion of descriptor distances for a given geographic distance should be minimal. The authors hypothesize that addressing these conditions will significantly reduce the probability of incorrect image retrieval orders, thereby improving recall metrics.

CliqueMining: A Novel Mining Strategy

CliqueMining is introduced to mitigate the GDS problems identified. The method involves the following steps:

1. Graph Creation: Construct a graph wherein vertices represent frames from visually similar sequences, and edges connect frames within a specified geographic threshold.
2. Place Sampling: Extract image sets or cliques from this graph, ensuring each set contains images geographically close to each other. These cliques form challenging training batches that enhance GDS.
3. Training Pipeline: Train VPR models using a combination of these challenging batches and other data sources (like GSV-Cities). The Multi-Similarity (MS) loss is employed for weighting and selecting hard positive and negative pairs dynamically during training.

Experimental Results

The effectiveness of CliqueMining was rigorously tested against state-of-the-art VPR models (DINOv2 SALAD and MixVPR) across several benchmarks:

• Nordland Dataset: Achieved a significant improvement in recall@1 from 76.0% to 90.7% with DINOv2 SALAD.
• MSLS Challenge: Observed a remarkable increase in recall@1 from 75.0% to 82.7%.
• MSLS Validation: Continued improvements in various recall metrics, indicating the robustness of CliqueMining in different environments.
• Pittsburgh-250k: Showed modest improvements, underscoring the dependency of CliqueMining's effectiveness on the density of data sampling.

The experiments demonstrated that models trained with CliqueMining exhibit a substantially improved GDS, particularly for small geographic distances. This improvement is graphically represented by steeper mean descriptor distances and lower variances in the descriptor-geographic distance plots.

Implications and Future Work

The practical implications of enhancing GDS through CliqueMining are substantial for applications requiring fine-grained localization. Improved recall metrics directly translate to higher reliability and accuracy in real-world deployments of VPR systems, such as autonomous vehicles, drones, and augmented reality devices.

Theoretically, the paper lays the foundation for further exploration into sophisticated mining strategies that can address other nuanced deficiencies in VPR and related fields. Future work may involve refining CliqueMining to adapt dynamically during training and exploring its applicability to more generalized image retrieval tasks.

Conclusion

The paper by Izquierdo and Civera marks a significant step forward in tackling the GDS issues in VPR models. CliqueMining emerges as a potent mechanism to train models that better correlate descriptor and geographic distances, especially in densely sampled environments. The demonstrated enhancements in recall metrics underscore the value of this approach, setting a new benchmark for future research in Visual Place Recognition.