Multi-Agent Pose Uncertainty: A Differentiable Rendering Cramér-Rao Bound

Abstract

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.

5 pages, 3 figures, 1 table. Presented at IEEE/CVF International Conference on Computer Vision (ICCV 2025) and IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025)