  Mesh-based scene representation offers a promising direction for simplifying
large-scale hierarchical visual localization pipelines, combining a visual
place recognition step based on global features (retrieval) and a visual
localization step based on local features. While existing work demonstrates the
viability of meshes for visual localization, the impact of using synthetic
databases rendered from them in visual place recognition remains largely
unexplored. In this work we investigate using dense 3D textured meshes for
large-scale Visual Place Recognition (VPR). We identify a significant
performance drop when using synthetic mesh-based image databases compared to
real-world images for retrieval. To address this, we propose MeshVPR, a novel
VPR pipeline that utilizes a lightweight features alignment framework to bridge
the gap between real-world and synthetic domains. MeshVPR leverages pre-trained
VPR models and is efficient and scalable for city-wide deployments. We
introduce novel datasets with freely available 3D meshes and manually collected
queries from Berlin, Paris, and Melbourne. Extensive evaluations demonstrate
that MeshVPR achieves competitive performance with standard VPR pipelines,
paving the way for mesh-based localization systems. Data, code, and interactive
visualizations are available at https://meshvpr.github.io/
