F2G-Pose: Geometry-Aware Foundation Feature Lifting for Direct RGB-D Category-Level Object Pose Estimation
F2G-Pose is a single-pass framework that predicts category-level 6D object pose, metric size, and camera-frame completed shape from one segmented RGB-D observation. It lifts frozen RADIO foundation features into partial point clouds, encodes local geometry with DGCNN tokenization, and fuses them with a Mixture-of-Experts Transformer. The method is trained on synthetic SOPE data and reports state-of-the-art accuracy on SOPE and ROPE at 28 FPS for pose-only inference, with transfer results on HouseCat6D and HANDAL.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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F2G-Pose is a single-pass framework that predicts category-level 6D object pose, metric size, and camera-frame completed shape from one segmented RGB-D observation. It lifts frozen RADIO foundation features into partial point clouds, encodes local geometry with DGCNN tokenization, and fuses them with a Mixture-of-Experts Transformer. The method is trained on synthetic SOPE data and reports state-of-the-art accuracy on SOPE and ROPE at 28 FPS for pose-only inference, with transfer results on HouseCat6D and HANDAL.
Key findings from the stored brief
- Predicts pose, metric size, and completed shape in one feed-forward pass from a single segmented RGB-D observation.
- Lifts frozen RADIO visual features to partial point clouds and fuses them with local geometry via a Mixture-of-Experts Transformer.
- Trained only on pose-annotated synthetic SOPE data; reports state-of-the-art accuracy on SOPE and ROPE.
- Reports 28 FPS for pose-only inference under its benchmark protocol and real-world manipulation experiments.
Source claims not independently checked; speed figures are stated under the paper's benchmark protocol.
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Why this may matter
Direct single-pass category-level pose estimation without CAD models or reference views could simplify robotic manipulation pipelines that need fast, reliable object pose and size. Joint shape completion aims to improve metric consistency.
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