论文标题
3D点云的设计模棱两可的神经网络
Design equivariant neural networks for 3D point cloud
论文作者
论文摘要
这项工作旨在通过在一般群体转换下引起群体均衡性来改善3D点云的现有神经网络的概括和鲁棒性。设计点云的模型模型时的主要挑战是如何权衡模型的性能和复杂性。现有的模型模型要么太复杂，无法实施或非常高的复杂性。这项研究的主要目的是建立一个通用程序，将群体模棱两可的属性引入3D点云的SOTA模型。构建的组模型我们的过程易于实施，与现有的模型相比，复杂性较小，并且它们保留了原始SOTA骨干的优势。从实验对象分类的结果来看，我们的方法优于性能和复杂性方面的其他组模型。此外，我们的方法还有助于改善语义分割模型的MIOU。总体而言，通过使用仅限限制性重视和增强性的组合，我们的模型可以优于现有的完整$ SO（3）$ - 等价模型，具有便宜的复杂性和GPU内存。所提出的程序是一般的，并构成了群体模化神经网络的基本方法。我们认为，将来它很容易适应其他SOTA模型。
This work seeks to improve the generalization and robustness of existing neural networks for 3D point clouds by inducing group equivariance under general group transformations. The main challenge when designing equivariant models for point clouds is how to trade-off the performance of the model and the complexity. Existing equivariant models are either too complicate to implement or very high complexity. The main aim of this study is to build a general procedure to introduce group equivariant property to SOTA models for 3D point clouds. The group equivariant models built form our procedure are simple to implement, less complexity in comparison with the existing ones, and they preserve the strengths of the original SOTA backbone. From the results of the experiments on object classification, it is shown that our methods are superior to other group equivariant models in performance and complexity. Moreover, our method also helps to improve the mIoU of semantic segmentation models. Overall, by using a combination of only-finite-rotation equivariance and augmentation, our models can outperform existing full $SO(3)$-equivariance models with much cheaper complexity and GPU memory. The proposed procedure is general and forms a fundamental approach to group equivariant neural networks. We believe that it can be easily adapted to other SOTA models in the future.
