Deep neural networks have enjoyed remarkable success for various vision
tasks, however it remains challenging to apply CNNs to domains lacking a
regular underlying structures such as 3D point clouds. Towards this we propose
a novel convolutional architecture, termed SpiderCNN, to efficiently extract
geometric features from point clouds. SpiderCNN is comprised of units called
SpiderConv, which extend convolutional operations from regular grids to
irregular point sets that can be embedded in R^n, by parametrizing a family of
convolutional filters. We design the filter as a product of a simple step
function that captures local geodesic information and a Taylor polynomial that
ensures the expressiveness. SpiderCNN inherits the multi-scale hierarchical
architecture from classical CNNs, which allows it to extract semantic deep
features. Experiments on ModelNet40 demonstrate that SpiderCNN achieves
state-of-the-art accuracy 92.4% on standard benchmarks, and shows competitive
performance on segmentation task.

本文提出一种新的卷积神经网络架构，名为 SpiderCNN，可用于提取点云中的几何特征。该架构采用称为 SpiderConv 的单元，通过对一组卷积滤波器进行参数化并将其扩展到不规则点集上，从而扩展了传统卷积操作的范围，并可用于不具有规则底层结构的域。我们的实验证明，SpiderCNN 在标准基准测试上的准确率为 92.4％，并且在分割任务上具有竞争性的性能。