  Existing view-based methods excel at recognizing 3D objects from predefined
viewpoints, but their exploration of recognition under arbitrary views is
limited. This is a challenging and realistic setting because each object has
different viewpoint positions and quantities, and their poses are not aligned.
However, most view-based methods, which aggregate multiple view features to
obtain a global feature representation, hard to address 3D object recognition
under arbitrary views. Due to the unaligned inputs from arbitrary views, it is
challenging to robustly aggregate features, leading to performance degradation.
In this paper, we introduce a novel Part-aware Network (PANet), which is a
part-based representation, to address these issues. This part-based
representation aims to localize and understand different parts of 3D objects,
such as airplane wings and tails. It has properties such as viewpoint
invariance and rotation robustness, which give it an advantage in addressing
the 3D object recognition problem under arbitrary views. Our results on
benchmark datasets clearly demonstrate that our proposed method outperforms
existing view-based aggregation baselines for the task of 3D object recognition
under arbitrary views, even surpassing most fixed viewpoint methods.
