Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications

TL;DR

This paper introduces a systematic procedural modeling and physically based rendering approach for generating highly realistic synthetic data to improve deep learning in automotive computer vision, outperforming traditional hand-crafted virtual worlds.

Contribution

A novel procedural world modeling method combined with physically accurate image synthesis for scalable, high-quality synthetic data generation in automotive applications.

Findings

Improves neural network performance in semantic segmentation tasks.

Achieves state-of-the-art results with modest implementation effort.

Provides flexible, scalable, and fully annotated synthetic datasets.

Abstract

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.