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Where is your place, Visual Place Recognition?

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arxiv 2103.06443 v2 pith:UREMHX3L submitted 2021-03-11 cs.RO cs.AIcs.CVcs.IRcs.LG

Sourav Garg , Tobias Fischer , Michael Milford This is my paper · ORCID
classification cs.ROcs.AIcs.CVcs.IRcs.LG
keywords placeagentvisualareasartificialdriversincludingintelligent
verification ladder T0 review T1 audit T2 compute T3 formal
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Visual Place Recognition (VPR) is often characterized as being able to recognize the same place despite significant changes in appearance and viewpoint. VPR is a key component of Spatial Artificial Intelligence, enabling robotic platforms and intelligent augmentation platforms such as augmented reality devices to perceive and understand the physical world. In this paper, we observe that there are three "drivers" that impose requirements on spatially intelligent agents and thus VPR systems: 1) the particular agent including its sensors and computational resources, 2) the operating environment of this agent, and 3) the specific task that the artificial agent carries out. In this paper, we characterize and survey key works in the VPR area considering those drivers, including their place representation and place matching choices. We also provide a new definition of VPR based on the visual overlap -- akin to spatial view cells in the brain -- that enables us to find similarities and differences to other research areas in the robotics and computer vision fields. We identify numerous open challenges and suggest areas that require more in-depth attention in future works.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Breaking D\'ej\`a Vu: Independent Auditing of Visual Place Recognition through Vision-Language Reasoning

    cs.CV 2026-07 unverdicted novelty 6.0 of 10

    VLM-based post-retrieval auditing of visual place recognition raises recall@1 by 13.6% on average while cutting false accepts to 12% and holding precision above 95%.

  2. Defending from GeoLocalization through Adversarial Road Trips

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    RoadTrip Attack uses beam search over adaptive geographic intermediate targets to produce stronger, more transferable, lower-visibility adversarial examples against retrieval-based image geolocalizers than PGD, FGSM, ...

  3. Long-Term Visual Localization in Dynamic Benthic Environments: A Dataset, Footprint-Based Ground Truth, and Visual Place Recognition Benchmark

    cs.CV 2026-03 conditional novelty 6.0 of 10

    A benchmark shows state-of-the-art visual place recognition performs poorly on a new multi-site, multi-year benthic AUV dataset, and that distance-based ground truth inflates recall.

  4. UniPR-3D: Towards Universal Visual Place Recognition with Visual Geometry Grounded Transformer

    cs.CV 2025-12 conditional novelty 6.0 of 10

    A VGGT-based descriptor merging 2D and 3D transformer tokens sets new state-of-the-art recall on single- and multi-frame visual place recognition benchmarks.

  5. EDTformer: An Efficient Decoder Transformer for Visual Place Recognition

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A decoder transformer with learnable queries plus a low-rank parallel adapter for frozen DINOv2 achieves state-of-the-art visual place recognition on multiple benchmarks with reduced training memory.

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