# Photorealism in Driving Simulations: Blending Generative Adversarial Image Synthesis with Rendering

arXiv: https://arxiv.org/abs/2007.15820 · HTML: https://mlsanity.com/paper/2007.15820/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [HaDR: Applying Domain Randomization for Generating Synthetic Multimodal Dataset for Hand Instance Segmentation in Cluttered Industrial Environments](https://mlsanity.com/paper/2304.05826/) (2023)

## Abstract (arXiv)

Driving simulators play a large role in developing and testing new intelligent vehicle systems. The visual fidelity of the simulation is critical for building vision-based algorithms and conducting human driver experiments. Low visual fidelity breaks immersion for human-in-the-loop driving experiments. Conventional computer graphics pipelines use detailed 3D models, meshes, textures, and rendering engines to generate 2D images from 3D scenes. These processes are labor-intensive, and they do not generate photorealistic imagery. Here we introduce a hybrid generative neural graphics pipeline for improving the visual fidelity of driving simulations. Given a 3D scene, we partially render only important objects of interest, such as vehicles, and use generative adversarial processes to synthesize the background and the rest of the image. To this end, we propose a novel image formation strategy to form 2D semantic images from 3D scenery consisting of simple object models without textures. These semantic images are then converted into photorealistic RGB images with a state-of-the-art Generative Adversarial Network (GAN) trained on real-world driving scenes. This replaces repetitiveness with randomly generated but photorealistic surfaces. Finally, the partially-rendered and GAN synthesized images are blended with a blending GAN. We show that the photorealism of images generated with the proposed method is more similar to real-world driving datasets such as Cityscapes and KITTI than conventional approaches. This comparison is made using semantic retention analysis and Frechet Inception Distance (FID) measurements.