论文标题
有效的场景压缩，用于基于视觉的本地化
Efficient Scene Compression for Visual-based Localization
论文作者
论文摘要
估计相机相对于3D重建或场景表示的姿势是许多混合现实和机器人应用程序的关键步骤。鉴于如今的大量可用数据，许多应用程序会限制存储和/或带宽有效工作。为了满足这些约束，许多应用程序通过减少其3D点的数量来压缩场景表示。尽管最先进的方法使用$ K $ -Cover的算法来压缩场景，但它们很缓慢且难以调节。为了提高速度和促进参数调整，这项工作引入了一种新颖的方法，该方法通过约束二次程序（QP）压缩场景表示。因为该QP类似于单级支持向量机，所以我们得出了顺序最小优化的变体来求解它。我们的方法使用与支持向量相对应的点作为代表场景的点的子集。我们还提出了一种有效的初始化方法，该方法允许我们的方法快速收敛。我们对公开数据集的实验表明，我们的方法在提供准确的姿势估计的同时，迅速压缩了场景表示。
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.
