SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation
CVPR 2024(2024)
摘要
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
complex dataset HouseCat6D which provides photometrically challenging objects,
SecondPose still surpasses other competitors by a large margin.
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