Using Auto-ML on Synthetic Point Cloud Generation

Abstract

Automated Machine Learning (Auto-ML) has primarily been used to optimize network hyperparameters or post-processing parameters, while the most critical component for training a high-quality model, the dataset, is usually left untouched. In this paper, we introduce a novel approach that applies Auto-ML methods to the process of generating synthetic datasets for training machine learning models. Our approach addresses the problem that generating synthetic datasets requires a complex data generator, and that developing and tuning a data generator for a specific scenario is a time-consuming and expensive task. Being able to reuse this data generator for multiple purposes would greatly reduce the effort and cost, once the process of tuning it to the specific domains of each task is automated. To demonstrate the potential of this idea, we have implemented a point cloud generator for simple scenes. The scenes from this generator can be used to train a neural network to semantically segment cars from the background. The simple composition of the scene allows us to reuse the generator for several different semantic segmentation tasks. The models trained on the datasets with the optimized domain parameters easily outperform a model without such optimizations, while the optimization effort is minimal due to our Auto-ML approach. Although the development of such complex data generators requires considerable effort, we believe that using Auto-ML for dataset creation has the potential to speed up the development of machine learning applications in domains where high-quality labeled data is difficult to obtain.

Keywords

• machine learning
• Auto-ML
• synthetic training data
• data generator
• domain parameter optimization
• high quality labeled data