Visual localization plays an important role in the applications of Augmented Reality (AR), which enable AR devices to obtain their 6-DoF pose in the pre-build map in order to render virtual content in real scenes. However, most existing approaches can not perform novel view rendering and require large storage capacities for maps. To overcome these limitations, we propose an efficient visual localization method capable of high-quality rendering with fewer parameters. Specifically, our approach leverages 3D Gaussian primitives as the scene representation. To ensure precise 2D-3D correspondences for pose estimation, we develop an unbiased 3D scene-specific descriptor decoder for Gaussian primitives, distilled from a constructed feature volume. Additionally, we introduce a salient 3D landmark selection algorithm that selects a suitable primitive subset based on the saliency score for localization. We further regularize key Gaussian primitives to prevent anisotropic effects, which also improves localization performance. Extensive experiments on two widely used datasets demonstrate that our method achieves superior or comparable rendering and localization performance to state-of-the-art implicit-based visual localization approaches.
视觉定位在增强现实（AR）应用中起着重要作用，它使得AR设备能够在预构建的地图中获取其6自由度（6-DoF）的位姿，以便在真实场景中渲染虚拟内容。然而，大多数现有的方法无法进行新视角渲染，并且需要大量存储空间用于存储地图。为克服这些限制，我们提出了一种高效的视觉定位方法，该方法能够以更少的参数实现高质量渲染。具体而言，我们的方法利用3D高斯基元作为场景表示。为了确保用于位姿估计的精确2D-3D对应关系，我们开发了一种无偏的3D场景特定描述符解码器，用于从构建的特征体积中提取高斯基元。此外，我们引入了一种显著的3D地标选择算法，通过显著性评分选择适合的基元子集用于定位。我们还对关键高斯基元进行了正则化处理，以防止各向异性效应，同时提高定位性能。基于两个广泛使用的数据集的实验结果表明，我们的方法在渲染和定位性能上优于或可与最先进的基于隐式表示的视觉定位方法相媲美。