Real-Time Panoptic Segmentation with Prototype Masks for Automated Driving

Abstract

In this paper we propose a fast fully convolutional neural network for panoptic segmentation that can provide an accurate semantic and instance-level representation of the environment in the 2D space. We tackle panoptic segmentation as a dense classification problem and generate masks for stuff classes as well as for each instance of things classes. Our network employs a shared backbone and Feature Pyramid Network for multi-scale feature extraction which we extend with dual-decoders that learn background and foreground specific masks. Guided by object proposals, the panoptic head assembles location-sensitive prototype masks using a learned weighting scheme. Our solution runs in real-time, in 82 ms on high resolution images, making it suitable for robotic applications and automated driving. Extensive experiments on the Cityscapes dataset demonstrate that our panoptic segmentation network is robust and accurate, with 57.3% PQ and 76.9% mIoU.