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
this https URL

本文提出了一种针对三维点云的新型深度架构，称为 SPE-Net，使用内嵌 “选择性位置编码（SPE）” 程序，依靠注意机制有效地关注输入的旋转条件，从而提高优化的性能和鲁棒性，并且相对于 SOTA 方法在旋转和非旋转的数据上都显示出明显的改善。