通过多视角特征匹配提升图像定位精度，兼顾速度与内存效率。
Multiview Image-Based Localization
- 分离平移与旋转估计，仅用相对平移求解相机位置
- 直接从多视图对应关系计算最优姿态，避免中间步骤误差
- 在7-Scenes和Cambridge Landmarks上表现优于现有方法
基于图像检索（IR）的图像定位方法相比3D和深度学习（DNN）方法具有无视角依赖、实现简单、无隐私问题且计算高效等优势，但其在相机位置与朝向估计上性能较差。本文提出一种混合方法：仅存储图像特征（类似IR），但利用潜在3D重建思想（类似3D方法），而不保留完整3D场景结构。核心思路包括：(i) 创新性地仅依赖相对平移估计来推断查询相机中心，不使用相对旋转估计；(ii) 从估计的相对姿态直接计算最优姿态，转为基于多视图对应关系求解，跳过“中间人”环节。该方法在7-Scenes和Cambridge Landmarks数据集上均取得更好性能，并在速度与内存占用上优于当前最先进水平。
原文摘要 · Abstract (English)
The image retrieval (IR) approach to image localization has distinct advantages to the 3D and the deep learning (DNN) approaches: it is seen-agnostic, simpler to implement and use, has no privacy issues, and is computationally efficient. The main drawback of this approach is relatively poor localization in both position and orientation of the query camera when compared to the competing approaches. This paper represents a hybrid approach that stores only image features in the database like some IR methods, but relies on a latent 3D reconstruction, like 3D methods but without retaining a 3D scene reconstruction. The approach is based on two ideas: {\em (i)} a novel proposal where query camera center estimation relies only on relative translation estimates but not relative rotation estimates through a decoupling of the two, and {\em (ii)} a shift from computing optimal pose from estimated relative pose to computing optimal pose from multiview correspondences, thus cutting out the ``middle-man''. Our approach shows improved performance on the 7-Scenes and Cambridge Landmarks datasets while also improving on timing and memory footprint as compared to state-of-the-art.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。