Simulation-based machine learning training data generation system

In recent years, machine learning has made considerable progress in various fields. The training of deep learning algorithm model requires a large amount of data, and a large amount of data annotation is very costly. In this case, the use of simulation data can provide great convenience. Using the three-dimensional reconstruction algorithm to construct a three-dimensional model of the real scene can speed up the construction of simulation data. The model has problems such as model damage and model fragmentation. The repair model takes a long time and the repair effect is poor. To solve these problems, we designed and developed a simulation training data generation system. The system uses Blender as a development platform, introduces a three-dimensional reconstruction algorithm to initialize the model, transforms the model into a point cloud for repair operation, and proposes a point cloud region adjustment method and a point cloud fast repair method. Finally, the refined model is used to generate a data set with true values. The experimental results show that the system we designed can improve the efficiency and accuracy of data generation and efficiently generate simulation training data for machine learning.