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

• URL: http://arxiv.org/abs/2510.21785v1
• Date: Sat, 18 Oct 2025 23:21:02 GMT
• Title: Multi-Agent Pose Uncertainty: A Differentiable Rendering Cramér-Rao Bound
• Authors: Arun Muthukkumar,
• Abstract summary: We derive a closed-form lower bound on the camera estimates by treating a differentiable pose as a perturbation measurement function.<n>Our approach reduces to classical bundle-adjustment uncertainty, ensuring continuity with vision theory.<n>It also naturally extends to multi-agent settings by fusing Fisher information across cameras.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 Cram\'er-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.

Related papers

• Single-image camera calibration with model-free distortion correction [0.0]
  This paper proposes a method for estimating the complete set of calibration parameters from a single image of a planar speckle pattern covering the entire sensor. The correspondence between image points and physical points on the calibration target is obtained using Digital Image Correlation. At the end of the procedure, a dense and uniform model-free distortion map is obtained over the entire image.
  arXiv  Detail & Related papers  (2024-03-02T16:51:35Z)
• Learning Robust Multi-Scale Representation for Neural Radiance Fields from Unposed Images [65.41966114373373]
  We present an improved solution to the neural image-based rendering problem in computer vision. The proposed approach could synthesize a realistic image of the scene from a novel viewpoint at test time.
  arXiv  Detail & Related papers  (2023-11-08T08:18:23Z)
• Single Image Depth Prediction Made Better: A Multivariate Gaussian Take [163.14849753700682]
  We introduce an approach that performs continuous modeling of per-pixel depth. Our method's accuracy (named MG) is among the top on the KITTI depth-prediction benchmark leaderboard.
  arXiv  Detail & Related papers  (2023-03-31T16:01:03Z)
• MELON: NeRF with Unposed Images in SO(3) [35.093700416540436]
  We show that a neural network can reconstruct a neural radiance field from unposed images with state-of-the-art accuracy while requiring ten times fewer views than adversarial approaches. Using a neural-network to regularize pose estimation, we demonstrate that our method can reconstruct a neural radiance field from unposed images with state-of-the-art accuracy while requiring ten times fewer views than adversarial approaches.
  arXiv  Detail & Related papers  (2023-03-14T17:33:39Z)
• Ambiguity-Aware Multi-Object Pose Optimization for Visually-Assisted Robot Manipulation [17.440729138126162]
  We present an ambiguity-aware 6D object pose estimation network, PrimA6D++, as a generic uncertainty prediction method. The proposed method shows a significant performance improvement in T-LESS and YCB-Video datasets. We further demonstrate real-time scene recognition capability for visually-assisted robot manipulation.
  arXiv  Detail & Related papers  (2022-11-02T08:57:20Z)
• Robustifying the Multi-Scale Representation of Neural Radiance Fields [86.69338893753886]
  We present a robust multi-scale neural radiance fields representation approach to overcome both real-world imaging issues. Our method handles multi-scale imaging effects and camera-pose estimation problems with NeRF-inspired approaches. We demonstrate, with examples, that for an accurate neural representation of an object from day-to-day acquired multi-view images, it is crucial to have precise camera-pose estimates.
  arXiv  Detail & Related papers  (2022-10-09T11:46:45Z)
• Deep Dual Consecutive Network for Human Pose Estimation [44.41818683253614]
  We propose a novel multi-frame human pose estimation framework, leveraging abundant temporal cues between video frames to facilitate keypoint detection. Our method ranks No.1 in the Multi-frame Person Pose Challenge Challenge on the large-scale benchmark datasets PoseTrack 2017 and PoseTrack 2018.
  arXiv  Detail & Related papers  (2021-03-12T13:11:27Z)
• Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation [74.76155168705975]
  Deep Bingham Networks (DBN) can handle pose-related uncertainties and ambiguities arising in almost all real life applications concerning 3D data. DBN extends the state of the art direct pose regression networks by (i) a multi-hypotheses prediction head which can yield different distribution modes. We propose new training strategies so as to avoid mode or posterior collapse during training and to improve numerical stability.
  arXiv  Detail & Related papers  (2020-12-20T19:20:26Z)
• Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation [89.82169646672872]
  We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori. We combine a classical geometric formulation with deep learning and extend the use of epipolar multi-rigid-body constraints to solve this task.
  arXiv  Detail & Related papers  (2020-11-30T20:46:48Z)
• 6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference [67.70859730448473]
  We present a multimodal camera relocalization framework that captures ambiguities and uncertainties. We predict multiple camera pose hypotheses as well as the respective uncertainty for each prediction. We introduce a new dataset specifically designed to foster camera localization research in ambiguous environments.
  arXiv  Detail & Related papers  (2020-04-09T20:55:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.