Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
This addresses the need for high-quality, cost-effective synthetic data in automotive applications, offering a systematic departure from hand-modeled methods, though it is incremental in its procedural and rendering improvements.
The authors tackled the problem of generating realistic synthetic data for training deep neural networks in computer vision, particularly for autonomous vehicles, by developing a procedural modeling and physically based rendering approach, which improved neural network performance and achieved state-of-the-art results with modest implementation efforts.
We present an overview and evaluation of a new, systematic approach for generation of highly realistic, annotated synthetic data for training of deep neural networks in computer vision tasks. The main contribution is a procedural world modeling approach enabling high variability coupled with physically accurate image synthesis, and is a departure from the hand-modeled virtual worlds and approximate image synthesis methods used in real-time applications. The benefits of our approach include flexible, physically accurate and scalable image synthesis, implicit wide coverage of classes and features, and complete data introspection for annotations, which all contribute to quality and cost efficiency. To evaluate our approach and the efficacy of the resulting data, we use semantic segmentation for autonomous vehicles and robotic navigation as the main application, and we train multiple deep learning architectures using synthetic data with and without fine tuning on organic (i.e. real-world) data. The evaluation shows that our approach improves the neural network's performance and that even modest implementation efforts produce state-of-the-art results.