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arXiv · 2008.00192

PanoNet: Real-time Panoptic Segmentation through Position-Sensitive Feature Embedding

Xia Chen·Jianren Wang·Martial Hebert
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Abstract

We propose a simple, fast, and flexible framework to generate simultaneously semantic and instance masks for panoptic segmentation. Our method, called PanoNet, incorporates a clean and natural structure design that tackles the problem purely as a segmentation task without the time-consuming detection process. We also introduce position-sensitive embedding for instance grouping by accounting for both object's appearance and its spatial location. Overall, PanoNet yields high panoptic quality results of high-resolution Cityscapes images in real-time, significantly faster than all other methods with comparable performance. Our approach well satisfies the practical speed and memory requirement for many applications like autonomous driving and augmented reality.

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Xia Chen, Jianren Wang, Martial Hebert. 2020-08-01. PanoNet: Real-time Panoptic Segmentation through Position-Sensitive Feature Embedding. https://arxiv.org/abs/2008.00192

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