简单有效的基于三元组的压缩策略用于紧凑视觉定位
Simple but Effective Triplet-Based Compression Strategies for Compact Visual Localization
提出一种基于三元组选择的简单SfM点云压缩策略，可直接用于视觉定位中减少场景表示的内存占用，且与乘积量化结合后性能优于近期学习方法。
原文依据 we focus on compressing the 3D structure of the scene by selecting a subset of points from a Structure-from-Motion (SfM) point cloud
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉定位，即估计拍摄图像时相机位姿的问题，是增强现实和自主机器人等应用的重要组成部分。许多此类应用需要紧凑的内存占用。因此，大量工作致力于为视觉定位设计内存高效的场景表示。本文关注通过从运动恢复结构（SfM）点云中选择点子集来压缩场景的三维结构。与旨在求解（复杂）优化问题的先前工作不同，我们提出了一种几乎无需实现的简单策略。我们的压缩策略基于选择点三元组的思想，使得从这些三元组能够准确估计每个数据库图像（用于构建SfM点云）的相机位姿。尽管简单，我们的策略表现与当前最优的结构压缩方法相当或更好。结合标准乘积量化方法压缩特征描述子，我们的方法优于近期基于学习的紧凑视觉定位方法。
英文原文摘要 1207 字符
Visual localization, i.e., the problem of estimating the camera pose from which an image was taken, is an important part of applications such as augmented reality and autonomous robots. Many of these applications require a compact memory footprint. Thus, a considerable amount of work has been spent on designing memory-efficient scene representations for visual localization. In this paper, we focus on compressing the 3D structure of the scene by selecting a subset of points from a Structure-from-Motion (SfM) point cloud. In contrast to prior work, which aims to solve (complex) optimization problems, we propose a simple strategy that is almost trivial to implement. Our compression strategy is based on the idea of selecting triplets of points such that the camera pose of each database image (used to build the SfM point cloud) can be accurately estimated from these triplets. Despite its simplicity, our strategy performs similarly to or better than current state-of-the-art structure compression approaches. Combined with standard product quantization approaches to compress feature descriptors, our approach compares favorably with recent learning-based approaches for compact visual localization.