# Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing

**Authors:** Magnus Wrenninge, Jonas Unger  
**arXiv:** [1810.08705](https://arxiv.org/abs/1810.08705)  
**Date:** 2018-10-19  
**Summary page:** https://www.summarizepaper.com/fr/paper/1810.08705v1/

## Abstract

We introduce Synscapes -- a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validation as well as new types of analysis. We study the behavior of networks trained on real data when performing inference on synthetic data: a key factor in determining the equivalence of simulation environments. We also compare the behavior of networks trained on synthetic data and evaluated on real-world data. Additionally, by analyzing pre-trained, existing segmentation and detection models, we illustrate how uncorrelated images along with a detailed set of annotations open up new avenues for analysis of computer vision systems, providing fine-grain information about how a model's performance changes according to factors such as distance, occlusion and relative object orientation.