用合成新视角提升视频定位识别准确率
Systematic Evaluation of Novel View Synthesis for Video Place Recognition
- 在五个公开数据集上系统测试合成视角对定位的影响
- 少量合成视角即可提升识别性能，数量比视角变化幅度更重要
- 适合做视觉定位、无人机与地面机器人协同的研习者
合成新视角有望在多个方面改善机器人导航。在基于图像的导航中，地面机器人拍摄场景生成的俯视图可用于引导无人机前往该位置。在视频位置识别（VPR）中，从空中生成的地面视角可帮助无人机识别地面机器人曾看到的位置，反之亦然。本文使用五个公开的VPR图像数据库和七种典型的图像相似性方法，系统评估了合成新视角在VPR中的表现。结果表明，在少量合成视角加入时，能有效提升VPR识别性能；而在大量添加时，视角变化幅度的影响小于所增加视角的数量以及数据集中图像类型。该研究验证了合成视角在增强跨视角匹配方面的潜力。
原文摘要 · Abstract (English)
The generation of synthetic novel views has the potential to positively impact robot navigation in several ways. In image-based navigation, a novel overhead view generated from a scene taken by a ground robot could be used to guide an aerial robot to that location. In Video Place Recognition (VPR), novel views of ground locations from the air can be added that enable a UAV to identify places seen by the ground robot, and similarly, overhead views can be used to generate novel ground views. This paper presents a systematic evaluation of synthetic novel views in VPR using five public VPR image databases and seven typical image similarity methods. We show that for small synthetic additions, novel views improve VPR recognition statistics. We find that for larger additions, the magnitude of viewpoint change is less important than the number of views added and the type of imagery in the dataset.
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