# Semantic Road Layout Understanding by Generative Adversarial Inpainting

arXiv: https://arxiv.org/abs/1805.11746 · HTML: https://mlsanity.com/paper/1805.11746/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2018.

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

- [Forecasting Future Instance Segmentation with Learned Optical Flow and Warping](https://mlsanity.com/paper/2211.08049/) (2022)

## Abstract (arXiv)

Autonomous driving is becoming a reality, yet vehicles still need to rely on complex sensor fusion to understand the scene they act in. The ability to discern static environment and dynamic entities provides a comprehension of the road layout that poses constraints to the reasoning process about moving objects. We pursue this through a GAN-based semantic segmentation inpainting model to remove all dynamic objects from the scene and focus on understanding its static components such as streets, sidewalks and buildings. We evaluate this task on the Cityscapes dataset and on a novel synthetically generated dataset obtained with the CARLA simulator and specifically designed to quantitatively evaluate semantic segmentation inpaintings. We compare our methods with a variety of baselines working both in the RGB and segmentation domains.