Abstract. 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 Rn
, 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 stateof-the-art accuracy 92.4% on standard benchmarks, and shows competitive performance on segmentation task.