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. The code will
be released soon.

通过整合物体特定的几何特征和 DINOv2 的语义类别先验知识，建立了一个新的方法 SecondPose 用于类别级别物体姿态估计，实验证明该方法在解决大量类内形状变化问题方面具有显著优势。