Semantic Road Layout Understanding by Generative Adversarial Inpainting
arXiv:1805.11746
Abstract
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.
References in corpus (13)
- Rethinking Atrous Convolution for Semantic Image Segmentation
- ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
- The Cityscapes Dataset for Semantic Urban Scene Understanding
- Holistically-Nested Edge Detection
- CARLA: An Open Urban Driving Simulator
- Generative Image Inpainting with Contextual Attention
- Playing for Data: Ground Truth from Computer Games
- SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting
- Video-to-Video Synthesis
- Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling
- Predicting Deeper into the Future of Semantic Segmentation
- Depth-aware CNN for RGB-D Segmentation
- Semi-parametric Image Synthesis