arXiv:2409.14067cs.CV2024-09被引 68

用3D高斯点云实现高效精准的AR定位与渲染

SplatLoc: 3D Gaussian Splatting-based Visual Localization for Augmented Reality

  • 用3D高斯原语表示场景,支持高质量新视角渲染
  • 通过显著性筛选关键点,定位精度优于主流方法
  • 适合需要低存储、实时渲染的AR应用开发者

视觉定位在增强现实(AR)中至关重要,使设备能在预构建地图中获取自身6-自由度位姿,从而在真实场景中渲染虚拟内容。然而,现有方法难以实现新视角渲染,且地图存储开销大。为此,我们提出一种基于3D高斯点云的高效视觉定位方法,可在参数更少的情况下实现高质量渲染。具体而言,利用3D高斯原语作为场景表示,设计了一种从构建的特征体中蒸馏出的无偏3D场景特定描述符解码器,以确保精确的2D-3D对应关系。此外,提出一种显著性3D关键点选择算法,根据显著性得分筛选合适的高斯原语子集用于定位。进一步对关键高斯原语进行正则化,防止各向异性效应,提升定位性能。在两个常用数据集上的大量实验表明,该方法在渲染和定位性能上均优于或媲美当前最先进的隐式表示方法。

原文摘要 · Abstract (English)

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. Project page: \href{https://zju3dv.github.io/splatloc}{https://zju3dv.github.io/splatloc}.

AR定位3D高斯视觉定位点云渲染

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