SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation

• URL: http://arxiv.org/abs/2311.11125v3
• Date: Fri, 22 Mar 2024 00:36:02 GMT
• Title: SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation
• Authors: Yamei Chen, Yan Di, Guangyao Zhai, Fabian Manhardt, Chenyangguang Zhang, Ruida Zhang, Federico Tombari, Nassir Navab, Benjamin Busam,
• Abstract summary: Category-level object pose estimation, aiming to predict the 6D pose and 3D size of objects from known categories, typically struggles with large intra-class shape variation. We present SecondPose, a novel approach integrating object-specific geometric features with semantic category priors from DINOv2.
• Score: 79.12683101131368
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Category-level object pose estimation, aiming to predict the 6D pose and 3D size of objects from known categories, typically struggles with large intra-class shape variation. Existing works utilizing mean shapes often fall short of capturing this variation. To address this issue, we present SecondPose, a novel approach integrating object-specific geometric features with semantic category priors from DINOv2. Leveraging the advantage of DINOv2 in providing SE(3)-consistent semantic features, we hierarchically extract two types of SE(3)-invariant geometric features to further encapsulate local-to-global object-specific information. These geometric features are then point-aligned with DINOv2 features to establish a consistent object representation under SE(3) transformations, facilitating the mapping from camera space to the pre-defined canonical space, thus further enhancing pose estimation. Extensive experiments on NOCS-REAL275 demonstrate that SecondPose achieves a 12.4% leap forward over the state-of-the-art. Moreover, on a more complex dataset HouseCat6D which provides photometrically challenging objects, SecondPose still surpasses other competitors by a large margin.

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