论文标题
四球：o（3）的神经描述符 - 引起的点云分析
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis
论文作者
论文摘要
在许多实际应用中，3D点云分析需要旋转不变性。在本文中，我们提出了3D旋转和反射下的可学习描述符不变性的，即O（3）动作，利用最近引入的最新的可检测的3D球形神经元和矢量神经元。具体而言，我们建议将3D球形神经元嵌入到4D载体神经元中，该神经元利用模型的端到端训练。在我们的方法中，我们执行四晶体 - 将3D输入的嵌入到4D中，由可进入的神经元构建，并提取更深的O（3）使用矢量神经元的e（3） - 等级特征。将四晶体的整合到VN-DGCNN框架中，称为四肽，可忽略的参数数量少于0.0002％。四球设置了一种新的最先进的性能，对Scanobjectnn充满挑战的子集的随机旋转现实对象进行分类。此外，四球比随机旋转的合成数据的所有模棱两可的方法：对对象进行分类，从模型Net40分类和分割Shapenet形状的部分。因此，我们的结果揭示了在3D Euclidean空间中学习的3D球形神经元的实际价值。该代码可从https://github.com/pavlo-melnyk/tetrasphere获得。
In many practical applications, 3D point cloud analysis requires rotation invariance. In this paper, we present a learnable descriptor invariant under 3D rotations and reflections, i.e., the O(3) actions, utilizing the recently introduced steerable 3D spherical neurons and vector neurons. Specifically, we propose an embedding of the 3D spherical neurons into 4D vector neurons, which leverages end-to-end training of the model. In our approach, we perform TetraTransform--an equivariant embedding of the 3D input into 4D, constructed from the steerable neurons--and extract deeper O(3)-equivariant features using vector neurons. This integration of the TetraTransform into the VN-DGCNN framework, termed TetraSphere, negligibly increases the number of parameters by less than 0.0002%. TetraSphere sets a new state-of-the-art performance classifying randomly rotated real-world object scans of the challenging subsets of ScanObjectNN. Additionally, TetraSphere outperforms all equivariant methods on randomly rotated synthetic data: classifying objects from ModelNet40 and segmenting parts of the ShapeNet shapes. Thus, our results reveal the practical value of steerable 3D spherical neurons for learning in 3D Euclidean space. The code is available at https://github.com/pavlo-melnyk/tetrasphere.
