多智能体位姿不确定性：一种可微渲染的Cramér-Rao下界
Multi-Agent Pose Uncertainty: A Differentiable Rendering Cramér-Rao Bound
AI总结：
本文提出一种可微渲染的Cramér-Rao下界，用于多智能体相机位姿不确定性量化，融合Fisher信息，无需关键点对应，适用于协作感知和新视图合成。
摘要：
位姿估计对于计算机视觉和机器人技术中的许多应用至关重要。尽管用途广泛，但很少有工作为密集或学习模型下的位姿提供严格的不确定性量化。我们通过将可微渲染器视为测量函数，推导出相机位姿估计协方差的闭式下界。在流形上相对于微小位姿扰动对图像形成进行线性化，得到一个渲染感知的Cramér-Rao下界。我们的方法简化为经典的束调整不确定性，确保与视觉理论的连续性。它还通过融合多台相机的Fisher信息自然扩展到多智能体设置。我们的统计公式为协作感知和新视图合成等任务提供了下游应用，而无需显式的关键点对应关系。
英文摘要：
Pose estimation is essential for many applications within computer vision and robotics. Despite its uses, few works provide rigorous uncertainty quantification for poses under dense or learned models. We derive a closed-form lower bound on the covariance of camera pose estimates by treating a differentiable renderer as a measurement function. Linearizing image formation with respect to a small pose perturbation on the manifold yields a render-aware Cramér-Rao bound. Our approach reduces to classical bundle-adjustment uncertainty, ensuring continuity with vision theory. It also naturally extends to multi-agent settings by fusing Fisher information across cameras. Our statistical formulation has downstream applications for tasks such as cooperative perception and novel view synthesis without requiring explicit keypoint correspondences.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
