评估VPR方法在图像对检索中的表现，助力机器人场景注册与建图。
Evaluation of Visual Place Recognition Methods for Image Pair Retrieval in 3D Vision and Robotics
- 将VPR用于图像对匹配，提升建图与定位精度。
- 现代全局描述子在复杂场景中表现优异，但效果因场景而异。
- 适合做视觉里程计、SLAM和三维重建的前处理模块。
视觉位置识别（VPR）是计算机视觉的核心技术，通常用于定位、建图和导航中的图像检索任务。本文将其视为注册流程的前端图像对检索问题，目标是从两组不重叠的图像中找出最优匹配的图像对，以支持后续的场景注册、SLAM和运动恢复结构（SfM）。我们在三个具有挑战性的数据集上对比评估了最先进的VPR方法：NetVLAD类基线、基于分类的全局描述子（CosPlace、EigenPlaces）、特征混合方法（MixVPR），以及基于基础模型的方法（AnyLoc、SALAD、MegaLoc）。结果表明，现代全局描述子在包含感知混淆和序列不完整等复杂场景下，已越来越适合作为开箱即用的图像对检索模块，但其性能表现出明显的领域依赖性，这对构建鲁棒映射与注册系统的选择至关重要。
原文摘要 · Abstract (English)
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.
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