Parallel Point Clouds: Point Clouds Generation and 3D Model Evolution via Virtual-real Interaction [平行点云:虚实互动的点云生成与三维模型进化方法]

作者：Tian Y.-L.; Shen Y.; Li Q.; Wang F.-Y.^*

来源：Acta Automatica Sinica, 2020, 46(12): 2572-2582.

DOI：10.16383/j.aas.c200800

• 3D object detection
• 3D vision models
• Parallel point clouds
• Virtual-real interaction

The extraction of 3D information is playing an increasingly important role in intelligent traffic scenes such as autonomous driving. In order to solve the problems faced by LiDAR sensor such as the high cost and incomplete coverage of possible scenarios, this paper proposes parallel point clouds and its framework. For parallel point clouds, virtual point clouds are obtained by building artiflcial scenes. Then 3D models are trained through computational experiments and tested by parallel execution. The evaluation results are fed back to the data generation and the training process of 3D models. Through continuous iteration, 3D models can be fully evaluated and updated. Under the framework of Parallel Point Clouds, we take the 3D object detection as an example and build a point clouds dataset in a closed-loop manner. Without human annotation, it can be used to effectively train the detection model which can achieve the 72% of the performance of model trained with annotated data.

• 单位
  中国科学院大学; 复杂系统管理与控制国家重点实验室; 中国科学技术大学