Domain Adaptation for Object Classification in Point Clouds via Asymmetrical Siamese and Conditional Adversarial Network

Abstract

Nowadays, researchers have developed various deep neural networks for processing point clouds effectively. Due to the enormous parameters in deep learning-based models, a lot of manual efforts have to be invested into annotating sufficient training samples. To mitigate such manual efforts of annotating samples for a new scanning device, this letter focuses on proposing a new neural network to achieve domain adaptation in 3D object classification. Specifically, to minimize the data discrepancy of intra-class objects in different domains, an Asymmetrical Siamese module is designed to align the intra-class features. To preserve the discriminative information for distinguishing inter-class objects in different domains, a Conditional Adversarial module is leveraged to consider the classification information conveyed from the classifier. To verify the effectiveness of the proposed method on object classification in heterogeneous point clouds, evaluations are conducted on three point cloud datasets, which are collected in different scenarios by different laser scanning devices. Furthermore, the comparative experiments also demonstrate the superior performance of the proposed method on the classification accuracy.