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

arXiv: https://arxiv.org/abs/1710.06270 · HTML: https://mlsanity.com/paper/1710.06270/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2017.

## Built on by (most cited first, 11 of 11)

- [Infinite Photorealistic Worlds using Procedural Generation](https://mlsanity.com/paper/2306.09310/) (2023)
- [GaRe: Relightable 3D Gaussian Splatting for Outdoor Scenes from Unconstrained Photo Collections](https://mlsanity.com/paper/2507.20512/) (2025)
- [SynFog: A Photo-realistic Synthetic Fog Dataset based on End-to-end Imaging Simulation for Advancing Real-World Defogging in Autonomous Driving](https://mlsanity.com/paper/2403.17094/) (2024)
- [XLD: A Cross-Lane Dataset for Benchmarking Novel Driving View Synthesis](https://mlsanity.com/paper/2406.18360/) (2024)
- [Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach](https://mlsanity.com/paper/2210.05626/) (2022)
- [Proc-GS: Procedural Building Generation for City Assembly with 3D Gaussians](https://mlsanity.com/paper/2412.07660/) (2024)
- [Learning Keypoints from Synthetic Data for Robotic Cloth Folding](https://mlsanity.com/paper/2205.06714/) (2022)
- [HazardNet: Road Debris Detection by Augmentation of Synthetic Models](https://mlsanity.com/paper/2303.07547/) (2023)
- [FSID: Fully Synthetic Image Denoising via Procedural Scene Generation](https://mlsanity.com/paper/2212.03961/) (2022)
- [View-Dependent Octree-based Mesh Extraction in Unbounded Scenes for Procedural Synthetic Data](https://mlsanity.com/paper/2312.08364/) (2023)
- [Position: Every Ground Truth is a Human Construction, not an Objective Truth](https://mlsanity.com/paper/2607.09668/) (2026)

## Abstract (arXiv)

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.