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Object Classification in Point Cloud Via Conditional Adversarial Domain Adaptation for Forest Inventory

Recently, laser scanning system is widely used to accurately predict forest inventory attributes. In this work, we propose an efficient framework including Feature Extractor Module and Conditional Adversarial Module for object classification in 3D heterogeneous point clouds. The Feature Extractor Module can aggregate features of objects in point clouds. For domain adaptation task, the Conditional Adversarial Module is proposed to minimize the discrepancy of source and target domains. Since there is no common evaluation benchmark for object classification in 3D point cloud, we build a bench-mark of six tasks from three 3D objects datasets. Evaluations on six tasks with four categories have demonstrated the effectiveness of our proposed framework of object classification. The framework can be applied potentially in forest management.