ENDOWING DEEP 3D MODELS WITH ROTATION INVARIANCE BASED ON PRINCIPAL COMPONENT ANALYSIS

In this paper, we propose to endow deep 3D models with rotation invariance by expressing the coordinates in an intrinsic frame determined by the object shape itself. Key to our approach is to find such an intrinsic frame which should be unique to the identical object shape and consistent across different instances of the same category. Interestingly, the principal component analysis exactly provides an effective way to define such a frame, i.e. setting the principal components as the frame axes. As the principal components have direction ambiguity, there exist several intrinsic frames for each object. To achieve absolute rotation invariance for a deep model, we adopt the coordinates expressed in all intrinsic frames as inputs to obtain multiple output features, which will be aggregated as a final feature via a self-attention module. Comprehensive experiments demonstrate that our approach can achieve state-of-the-art performance on rotated 3D object classification and retrieval tasks.