CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild

Published in CVPR, 2022

This paper addresses category-level 9D pose estimation in the wild using a single RGB-D frame. Inspired by traditional point pair features (PPFs), we introduce a novel Category-level PPF (CPPF) voting method for accurate, robust, and generalizable 9D pose estimation. Our approach samples numerous point pairs on an object, predicting SE(3)-invariant voting statistics for object centers, orientations, and scales. We propose a coarse-to-fine voting algorithm to filter out noisy samples and refine predictions. An auxiliary binary classification task helps eliminate false positives in orientation voting. To ensure robustness, our sim-to-real pipeline trains on synthetic point clouds, except for geometrically ambiguous objects.

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