6DOPE-GS：使用高斯泼溅的在线6D物体姿态估计
6DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splatting
AI总结：
提出6DOPE-GS方法，利用高斯泼溅实现单RGB-D相机在线6D物体姿态估计与跟踪，通过增量2D高斯泼溅和动态关键帧选择实现高效重建，性能匹配基线且速度提升5倍。
摘要：
高效且准确的物体姿态估计是增强现实、自动驾驶和机器人等应用中现代视觉系统的重要组成部分。尽管基于模型的6D物体姿态估计研究已取得可喜成果，但无模型方法在实时RGB-D视频流中渲染和推断任意物体一致姿态时，仍受制于高计算负荷。为解决此问题，我们提出6DOPE-GS，一种利用高斯泼溅（Gaussian Splatting）进展的新型单RGB-D相机在线6D物体姿态估计与跟踪方法。得益于高斯泼溅快速的可微渲染能力，6DOPE-GS能同时优化6D物体姿态和3D物体重建。为实现实时跟踪所需的效率与精度，该方法采用增量2D高斯泼溅与智能动态关键帧选择程序，以实现高空间物体覆盖率并防止错误姿态更新。我们还提出基于不透明度统计的剪枝机制用于自适应高斯密度控制，确保训练稳定性与效率。我们在HO3D和YCBInEOAT数据集上评估了该方法，结果表明6DOPE-GS在无模型同时6D姿态跟踪与重建方面，性能匹配最先进基线，同时提供5倍加速。我们还展示了该方法在真实场景中适用于实时动态物体跟踪与重建。
英文摘要：
Efficient and accurate object pose estimation is an essential component for modern vision systems in many applications such as Augmented Reality, autonomous driving, and robotics. While research in model-based 6D object pose estimation has delivered promising results, model-free methods are hindered by the high computational load in rendering and inferring consistent poses of arbitrary objects in a live RGB-D video stream. To address this issue, we present 6DOPE-GS, a novel method for online 6D object pose estimation \& tracking with a single RGB-D camera by effectively leveraging advances in Gaussian Splatting. Thanks to the fast differentiable rendering capabilities of Gaussian Splatting, 6DOPE-GS can simultaneously optimize for 6D object poses and 3D object reconstruction. To achieve the necessary efficiency and accuracy for live tracking, our method uses incremental 2D Gaussian Splatting with an intelligent dynamic keyframe selection procedure to achieve high spatial object coverage and prevent erroneous pose updates. We also propose an opacity statistic-based pruning mechanism for adaptive Gaussian density control, to ensure training stability and efficiency. We evaluate our method on the HO3D and YCBInEOAT datasets and show that 6DOPE-GS matches the performance of state-of-the-art baselines for model-free simultaneous 6D pose tracking and reconstruction while providing a 5$\times$ speedup. We also demonstrate the method's suitability for live, dynamic object tracking and reconstruction in a real-world setting.
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