Efficient Scene Compression for Visual-based Localization
Efficient Scene Compression for Visual-based Localization
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DOI：
10.1109/3dv50981.2020.00111
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Marcela Mera-Trujillo;Benjamin A. Smith;Victor Fragoso
中科院分区：
文献类型：
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作者：
Marcela Mera-Trujillo;Benjamin A. Smith;Victor Fragoso
估计相机相对于3D重建或场景表示的姿态对于许多混合现实和机器人应用来说是至关重要的一步。考虑到当今大量的可用数据，许多应用限制存储和/或带宽以有效地工作。为了满足这些约束，许多应用通过减少其3D点的数量来压缩场景表示。虽然最先进的方法使用基于K覆盖的算法来压缩场景，但它们速度很慢，而且很难调整。为了提高速度和方便参数调整，这项工作介绍了一种新的方法，压缩场景表示的约束二次规划（QP）。由于这种QP类似于一类支持向量机，我们推导出一种序列最小优化的变体来解决它。我们的方法使用对应于支持向量的点作为点的子集来表示场景。我们还提出了一个有效的初始化方法，使我们的方法快速收敛。我们在公开数据集上的实验表明，我们的方法可以快速压缩场景表示，同时提供准确的姿态估计。
Estimating the pose of a camera with respect to a 3D reconstruction or scene representation is a crucial step for many mixed reality and robotics applications. Given the vast amount of available data nowadays, many applications constrain storage and/or bandwidth to work efficiently. To satisfy these constraints, many applications compress a scene representation by reducing its number of 3D points. While state-of-the-art methods use K-cover-based algorithms to compress a scene, they are slow and hard to tune. To enhance speed and facilitate parameter tuning, this work introduces a novel approach that compresses a scene representation by means of a constrained quadratic program (QP). Because this QP resembles a one-class support vector machine, we derive a variant of the sequential minimal optimization to solve it. Our approach uses the points corresponding to the support vectors as the subset of points to represent a scene. We also present an efficient initialization method that allows our method to converge quickly. Our experiments on publicly available datasets show that our approach compresses a scene representation quickly while delivering accurate pose estimates.