Evaluation of Visual Place Recognition Methods for Image Pair Retrieval in 3D Vision and Robotics

Dennis Haitz; Athradi Shritish Shetty; Michael Weinmann; Markus Ulrich

cs.CVMar 14, 2026

Evaluation of Visual Place Recognition Methods for Image Pair Retrieval in 3D Vision and Robotics

Authors: Dennis Haitz, Athradi Shritish Shetty, Michael Weinmann, Markus Ulrich

Organizations: Karlsruhe Institute of Technology, Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany · Delft University of Technology, Department of Intelligent Systems, The Netherlands

Abstract

Visual Place Recognition (VPR) is a core component in computer vision, typically formulated as an image retrieval task for localization, mapping, and navigation. In this work, we instead study VPR as an image pair retrieval front-end for registration pipelines, where the goal is to find top-matching image pairs between two disjoint image sets for downstream tasks such as scene registration, SLAM, and Structure-from-Motion. We comparatively evaluate state-of-the-art VPR families - NetVLAD-style baselines, classification-based global descriptors (CosPlace, EigenPlaces), feature-mixing (MixVPR), and foundation-model-driven methods (AnyLoc, SALAD, MegaLoc) - on three challenging datasets: object-centric outdoor scenes (Tanks and Temples), indoor RGB-D scans (ScanNet-GS), and autonomous-driving sequences (KITTI). We show that modern global descriptor approaches are increasingly suitable as off-the-shelf image pair retrieval modules in challenging scenarios including perceptual aliasing and incomplete sequences, while exhibiting clear, domain-dependent strengths and weaknesses that are critical when choosing VPR components for robust mapping and registration.

Figures & tables

Panel of Figure 1 — Figure 1: Example images from sets A (left) and B (right), taken from the Caterpillar scene of the Tanks and Temples dataset ( Knapitsch et al., 2017 ) . Images including overlapping scene area are framed in red, the objective in our contribution is to test the ability of VPR methods to retrieve these pairs. Challenging for VPR models in such scenes is the perceptual aliasing, e.g. indicated by the wheels, which are visible on both sides and even appear in similar relative positions.

Method	Backbone	Descriptor size
CosPlace512	ResNet18	512
CosPlace2048	ResNet101	2048
EigenPlaces512	ResNet18	512
EigenPlaces2048	ResNet101	2048
MixVPR512	ResNet50	512
MixVPR4096	ResNet50	4096

Table 1: Different backbone and descriptor size configurations used for the methods CosPlace ( Berton et al., 2022 ) , EigenPlaces ( Berton et al., 2023 ) , MixVPR ( Ali-Bey et al., 2023 ) , and SALAD ( Izquierdo and Civera, 2024 ) .

Panel of Figure 2 — Figure 2: Qualitative top-5 retrieval results for different VPR methods on the T&T scene Barn . This scene is especially prone to perceptual aliasing, as indicated by the brown door, which is at different positions on both sides and the color of the floor in front of the house. Overlap areas are the back of the house, which is completely correctly retrieved by SALAD8192 and MixVPR4096. Columns correspond to methods (a–f) and, within each column, images are shown from rank $k=1$ (top) to $k=5$ (bottom).

Panel of Figure 3 — Figure 3: Qualitative Top-5 retrieval results from CosPlace512 (a) and MegaLoc (b) for KITTI scene 07_02. The challenge here is that some images were left out between $A$ and $B$ . This is especially important for tasks such as SLAM relocalization caused from missing frames. CosPlace ( Berton et al., 2022 ) completely fails here, likely because of perceptual aliasing, indicated by two different black cars in $A$ and $B$ and other similarities, whereas MegaLoc ( Berton and Masone, 2025 ) yields only true positives. The cue for correct results is the houses in the center of the $A$ images, which appear towards the left border of the $B$ images. Images are shown from $k=1$ (top) to $k=5$ (bottom) per method.

Dataset	Method	P@1	P@5	P@10	R@1	R@5	R@10	mAP@5	mAP@10	$\mu_{t}$	$\sigma_{t}$
	PatchNetVLAD	25.00	21.25	17.50	25.00	37.50	43.75	25.10	26.27	1.102	0.105
	CosPlace512	56.25	43.75	36.88	56.25	56.25	62.50	55.62	52.69	0.133	0.014
	CosPlace2048	56.25	40.00	32.50	56.25	56.25	62.50	53.33	51.54	0.645	0.070
	EigenPlaces512	56.25	47.50	36.88	56.25	56.25	62.50	54.37	54.35	0.134	0.016
	EigenPlaces2048	56.25	45.00	35.62	56.25	62.50	62.50	57.19	53.67	0.649	0.070
T&T	MixVPR512	18.75	6.25	5.00	18.75	25.00	31.25	21.88	21.56	0.130	0.012

Table 2: Image pair retrieval across different datasets, consisting of multiple scenes (quantity in braces) for each dataset: T&T (16), SN-GS (26), and KITTI (26). Each dataset is evaluated on seven VPR methods, split into eleven configurations. Metrics are calculated for the

k

best ranked image pairs, leading to Precision@k (P@k), Recall@k (R@k), and mean-Average-Precision@k (mAP@k), all given in percent. Average time in seconds for all retrievals per scene is included as

\mu_{t}

with

\sigma_{t}

as the standard deviation, respectively.

Figure 4: Overview of P@10 (left column) and R@10 (right column) for all VPR methods on T&T (top row), SN-GS (middle row) and KITTI (bottom row).

Visual Place Recognition

Visual Place Recognition matches camera observations to mapped places despite changes in viewpoint, lighting, weather, or time.

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May 29, 2026J/K move · Enter open · S save

DisPlace: Discriminative Place Projections for Multi-Reference Visual Place Recognition

Dhyey Manish Rajani, Michael Milford, Tobias Fischer

QUT Centre for Robotics, School of Electrical Engineering and Robotics at the Queensland University of Technology.

Visual Place Recognition Feature Fusion

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A key challenge in Visual Place Recognition (VPR) is matching query images against reference maps captured under diverse environmental conditions and viewpoints. While multiple reference traversals improve robustness, existing fusion strategies either aggregate references uniformly or rely on heuristic selection, without distinguishing descriptor variations that preserve stable place identity from those caused by changing conditions or viewpoints. In this paper, we propose DisPlace, a multi-reference VPR framework that fuses multiple reference descriptors into a single compact and discriminative place representation. DisPlace formulates descriptor fusion as a generalized eigenvalue problem that maximizes between-place separability while suppressing within-place variation across references, rather than preserving overall descriptor variance. Unlike existing multi-reference fusion methods, DisPlace exploits variation across reference traversals to identify which linear combinations of descriptor dimensions preserve place identity and which capture condition- or viewpoint-specific variation. We evaluate DisPlace on Oxford RobotCar, Nordland, Pittsburgh30k, and Google Landmarks v2 across six state-of-the-art VPR descriptors. DisPlace outperforms seven multi-reference baselines in 49 out of 54 appearance-varying conditions, consistently improves descriptor-level fusion performance under viewpoint and unstructured settings, and requires less storage during inference than all compared fusion methods.

Abstract

Visual Place Recognition

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