Unsupervised Traffic Scene Generation with Synthetic 3D Scene Graphs

Understand

Image synthesis driven by computer graphics achieved recently a remarkable realism, yet synthetic image data generated this way reveals a significant domain gap with respect to real-world data.

• This is especially true in autonomous driving scenarios, which represent a critical aspect for overcoming utilizing synthetic data for training neural networks.
• We propose a method based on domain-invariant scene representation to directly synthesize traffic scene imagery without rendering.
• Specifically, we rely on synthetic scene graphs as our internal representation and introduce an unsupervised neural network architecture for realistic traffic scene synthesis.