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) and identify a significant
performance drop when using synthetic mesh-based 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 it 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. Our contributions include
the new task of citywide mesh-based VPR, the new benchmark datasets, MeshVPR,
and a thorough analysis of open challenges. Data, code, and interactive
visualizations are available at this https URL

通过使用稠密的三维纹理网格进行大规模视觉地点识别，我们发现使用合成基于网格的数据库进行视觉地点识别相比于真实世界图像的检索存在显著性能下降。为了解决这个问题，我们提出了 MeshVPR，这是一种利用轻量级特征对齐框架来弥合真实世界和合成领域差距的新型视觉地点识别管道。通过引入柏林、巴黎和墨尔本的新型数据集，我们进行了广泛的评估，证明了 MeshVPR 在标准视觉地点识别管道方面具有竞争力的性能，为基于网格的定位系统铺平了道路。