  In this paper, we propose a novel deep architecture tailored for 3D point
cloud applications, named as SPE-Net. The embedded ``Selective Position
Encoding (SPE)'' procedure relies on an attention mechanism that can
effectively attend to the underlying rotation condition of the input. Such
encoded rotation condition then determines which part of the network parameters
to be focused on, and is shown to efficiently help reduce the degree of freedom
of the optimization during training. This mechanism henceforth can better
leverage the rotation augmentations through reduced training difficulties,
making SPE-Net robust against rotated data both during training and testing.
The new findings in our paper also urge us to rethink the relationship between
the extracted rotation information and the actual test accuracy. Intriguingly,
we reveal evidences that by locally encoding the rotation information through
SPE-Net, the rotation-invariant features are still of critical importance in
benefiting the test samples without any actual global rotation. We empirically
demonstrate the merits of the SPE-Net and the associated hypothesis on four
benchmarks, showing evident improvements on both rotated and unrotated test
data over SOTA methods. Source code is available at
https://github.com/ZhaofanQiu/SPE-Net.
