论文标题
旋转不变的点卷积与多个模棱两可的对齐
Rotation-Invariant Point Convolution With Multiple Equivariant Alignments
论文作者
论文摘要
最近在3D深度学习方法中引入旋转不变性或均衡性的尝试显示出令人鼓舞的结果，但是这些方法仍然很难达到标准的3D神经网络的性能。在这项工作中，我们研究了3D点卷积中的均等和不变性之间的关系。我们表明，使用旋转等值的比对，可以使任何卷积层旋转不变。此外，我们通过使用对齐本身作为卷积的特征，并将多个对齐结合在一起，从而改善了这种简单的对齐过程。使用此核心层，我们设计了旋转不变的架构，从而改善了最新的架构，从而导致对象分类和语义分割，并减少旋转不变和标准3D深度学习方法之间的差距。
Recent attempts at introducing rotation invariance or equivariance in 3D deep learning approaches have shown promising results, but these methods still struggle to reach the performances of standard 3D neural networks. In this work we study the relation between equivariance and invariance in 3D point convolutions. We show that using rotation-equivariant alignments, it is possible to make any convolutional layer rotation-invariant. Furthermore, we improve this simple alignment procedure by using the alignment themselves as features in the convolution, and by combining multiple alignments together. With this core layer, we design rotation-invariant architectures which improve state-of-the-art results in both object classification and semantic segmentation and reduces the gap between rotation-invariant and standard 3D deep learning approaches.
