Revisit Anything: Visual Place Recognition via Image Segment Retrieval (2409.18049v1)

Abstract: Accurately recognizing a revisited place is crucial for embodied agents to localize and navigate. This requires visual representations to be distinct, despite strong variations in camera viewpoint and scene appearance. Existing visual place recognition pipelines encode the "whole" image and search for matches. This poses a fundamental challenge in matching two images of the same place captured from different camera viewpoints: "the similarity of what overlaps can be dominated by the dissimilarity of what does not overlap". We address this by encoding and searching for "image segments" instead of the whole images. We propose to use open-set image segmentation to decompose an image into meaningful' entities (i.e., things and stuff). This enables us to create a novel image representation as a collection of multiple overlapping subgraphs connecting a segment with its neighboring segments, dubbed SuperSegment. Furthermore, to efficiently encode these SuperSegments into compact vector representations, we propose a novel factorized representation of feature aggregation. We show that retrieving these partial representations leads to significantly higher recognition recall than the typical whole image based retrieval. Our segments-based approach, dubbed SegVLAD, sets a new state-of-the-art in place recognition on a diverse selection of benchmark datasets, while being applicable to both generic and task-specialized image encoders. Finally, we demonstrate the potential of our method to`revisit anything'' by evaluating our method on an object instance retrieval task, which bridges the two disparate areas of research: visual place recognition and object-goal navigation, through their common aim of recognizing goal objects specific to a place. Source code: https://github.com/AnyLoc/Revisit-Anything.

• The paper introduces SuperSegments, combining overlapping segments to enhance recognition of partially captured scenes.
• It develops a factorized feature aggregation technique that encodes segment details into compact descriptor vectors.
• The similarity-weighted ranking system converts segment-level matches into robust image-level retrieval, achieving state-of-the-art recall metrics.

Revisit Anything: Visual Place Recognition via Image Segment Retrieval

The paper "Revisit Anything: Visual Place Recognition via Image Segment Retrieval" presents a novel methodology for visual place recognition (VPR) by leveraging image segments rather than whole images. The authors propose a new approach named SegVLAD which decomposes images into meaningful segments and uses these segments for place recognition. This approach addresses inherent challenges in VPR due to viewpoint variations and appearance changes by focusing on the retrieval of partial image representations.

Key Contributions

1. Introduction of SuperSegments: The authors introduce the concept of SuperSegments, which are formed by combining individual image segments with their neighboring segments. This creates overlapping subgraphs that encapsulate more context compared to isolated segments, thereby enhancing the accuracy of recognizing partially overlapping images.
2. Novel Feature Aggregation via Factorized Representation: The SegVLAD approach includes a factorized representation technique for aggregating segment features efficiently. This method allows for effective encoding of segmented image information into compact vector representations suitable for retrieval tasks.
3. Similarity-Weighted Ranking Method: To convert segment-level retrievals into image-level retrievals, a similarity-weighted ranking method is proposed. This technique ranks query images based on the cumulative similarity scores of their constituent segments, allowing for more precise image matching in VPR contexts.

Numerical Performance and Analysis

The SegVLAD methodology is benchmarked against several state-of-the-art VPR techniques, including CosPlace, MixVPR, EigenPlaces, AnyLoc, and SALAD. Across various datasets covering both indoor and outdoor environments, SegVLAD achieves superior Recall@1 and Recall@5 metrics, demonstrating its robust performance in recognizing places under various conditions:

• Outdoor Street-View Datasets:

SegVLAD (using a finetuned DINOv2 backbone) outperforms other methods on datasets such as Pitts30K, MSLS, and SF-XL. When compared to global descriptor-based approaches, SegVLAD shows significant improvements in recall metrics, highlighting the effectiveness of segment-based retrieval under wide viewpoint variations.

• Out-of-Distribution Datasets:

SegVLAD also sets a new state-of-the-art on datasets such as Baidu Mall, AmsterTime, and InsideOut, which are characterized by strong appearance shifts, clutter, and viewpoint variations. This robustness across varied contexts underlines the general applicability and strength of the proposed method.

Implications and Future Developments

The innovative use of SuperSegments and factorized feature aggregation in SegVLAD represents a pivotal shift in VPR research. By moving away from holistic image descriptors to partial segment representations, the method addresses a fundamental challenge in matching partially overlapping images from different viewpoints. This has several practical implications:

• Enhanced Autonomous Navigation:

Embodied agents and autonomous vehicles can benefit from improved VPR capabilities, enabling more accurate localization and navigation, especially in dynamic or cluttered environments.

• Application in Object Instance Retrieval:

The paper extends SegVLAD's application to an object instance retrieval task, demonstrating its ability to recognize specific target objects within broader scenes. This bridges visual place recognition with object-goal navigation, which is critical for tasks such as semantic-driven navigation and mobile robotics.

The paper speculates that future VPR systems may further integrate segments-based retrieval with hierarchical reranking methods like MESA. Additionally, the method’s open-set nature and compatibility with segmentation models like SAM extend its usability in open-set recognition scenarios, facilitating integration with textual interfaces based on models like CLIP and GPT.

Overall, "Revisit Anything: Visual Place Recognition via Image Segment Retrieval" introduces a compelling new direction in VPR research, effectively utilizing segmented visual representations to achieve higher accuracy and robustness in challenging recognition tasks. This work not only advances the current state of visual localization but also sets the stage for future research in integrating semantic understanding and environmental context into visual AI systems.