CVPR 2023, highlight paper - Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation

In this episode we discuss Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation by Heng Yang, Marco Pavone. The paper proposes a two-stage object pose estimation method that uses conformal keypoint detection and geometric uncertainty propagation to endow an estimation with provable and computable worst-case error bounds. Conformal keypoint detection converts heuristic detections into circular or elliptical prediction sets that cover the groundtruth keypoints with a user-specified marginal probability. Geometric uncertainty propagation propagates geometric constraints to the 6D object pose, leading to a Pose UnceRtainty SEt (PURSE) that guarantees coverage of the groundtruth pose with the same probability. The proposed method achieves better or similar accuracy as existing methods on the LineMOD Occlusion dataset while providing correct uncertainty quantification.