仅用一张彩色图实现高精度6自由度姿态估计
SplatPose: Geometry-Aware 6-DoF Pose Estimation from Single RGB Image via 3D Gaussian Splatting
- 用3D高斯点云与双分支网络联合建模，解耦位置与角度对齐
- 在三个基准数据集上达到当前单图方法最优性能
- 适合需低成本部署的AR、机器人场景
6-DoF姿态估计是计算机视觉中的基础任务，广泛应用于增强现实和机器人领域。现有基于单张彩色图像的方法常因依赖初始姿态估计及易受旋转歧义影响而降低精度，而需要深度传感器或多视角设置的方法则带来显著部署成本。为此，本文提出SplatPose，一种融合3D高斯点云（3DGS）与双分支神经架构的新框架，仅通过单张RGB图像即可实现高精度姿态估计。核心在于双注意力射线评分网络（DARS-Net），通过几何域注意力机制创新性地解耦位置与角度对齐，显式建模方向依赖关系以缓解旋转歧义。此外，采用粗到精优化流程，通过查询图像与3DGS合成视图间的密集2D特征对齐，逐步修正稀疏射线采样带来的特征错位与深度误差。在三个基准数据集上的实验表明，SplatPose在单图设置下达到当前最优6-DoF姿态估计精度，媲美依赖深度或多视角信息的方法。
原文摘要 · Abstract (English)
6-DoF pose estimation is a fundamental task in computer vision with wide-ranging applications in augmented reality and robotics. Existing single RGB-based methods often compromise accuracy due to their reliance on initial pose estimates and susceptibility to rotational ambiguity, while approaches requiring depth sensors or multi-view setups incur significant deployment costs. To address these limitations, we introduce SplatPose, a novel framework that synergizes 3D Gaussian Splatting (3DGS) with a dual-branch neural architecture to achieve high-precision pose estimation using only a single RGB image. Central to our approach is the Dual-Attention Ray Scoring Network (DARS-Net), which innovatively decouples positional and angular alignment through geometry-domain attention mechanisms, explicitly modeling directional dependencies to mitigate rotational ambiguity. Additionally, a coarse-to-fine optimization pipeline progressively refines pose estimates by aligning dense 2D features between query images and 3DGS-synthesized views, effectively correcting feature misalignment and depth errors from sparse ray sampling. Experiments on three benchmark datasets demonstrate that SplatPose achieves state-of-the-art 6-DoF pose estimation accuracy in single RGB settings, rivaling approaches that depend on depth or multi-view images.
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