Abstract
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. We enhance synthetic scene graphs with spatial information about the scene and demonstrate the effectiveness of our approach through scene manipulation.
Abstract (translated)
以计算机图形驱动的图像合成最近取得了惊人的真实感,但这种方式生成的合成图像数据揭示了与现实世界数据之间存在的重大领域差异。这在自动驾驶场景中尤为重要,这代表了克服利用合成数据训练神经网络的关键问题之一。我们提出了一种基于领域无关场景表示的方法,直接合成交通场景图像而不需要进行渲染。具体来说,我们依靠合成场景图作为内部表示,并引入无监督神经网络架构来实现逼真的交通场景合成。我们利用场景的空间信息进行增强,并通过场景操纵来展示我们的方法的有效性。
URL
https://arxiv.org/abs/2303.08473
