ERINet: Enhanced rotation-invariant network for point cloud classification

Abstract

Abstract Point cloud classification has attracted increasing attention due to the outstanding performance of elaborated networks on synthetic datasets. However, rotation invariance has been seldom investigated. In this paper, we propose a straightforward rotation-invariant network called ERINet with a novel enhanced rotation-invariant module for point cloud classification. The enhanced rotation-invariant module is composed of a representation conversion component and a feature aggregation layer. It first takes 12 well-designed rotation-invariant features as the representation of point cloud and leverages the feature aggregation layer to aggregate the features of neighbor points into a discriminative rotation-invariant representation. The enhanced rotation-invariant module is further combined with the multi-layer perceptron and the fully connected layers to form an efficient ERINet. The proposed ERINet demonstrated its advantages with a small model size and high speed. The enhanced rotation-invariant module of our ERINet is also extensible and can be easily integrated with mainstream networks to improve rotation robustness. The experimental results on rotation-augmented datasets demonstrate that our ERINet outperforms other state-of-the-art methods in rotation robustness for point cloud classification.