Abstract
In recent years, compact and efficient scene understanding representations have gained popularity in increasing situational awareness and autonomy of robotic systems. In this work, we illustrate the concept of a panoptic edge segmentation and propose PENet, a novel detection network called that combines semantic edge detection and instance-level perception into a compact panoptic edge representation. This is obtained through a joint network by multi-task learning that concurrently predicts semantic edges, instance centers and offset flow map without bounding box predictions exploiting the cross-task correlations among the tasks. The proposed approach allows extending semantic edge detection to panoptic edge detection which encapsulates both category-aware and instance-aware segmentation. We validate the proposed panoptic edge segmentation method and demonstrate its effectiveness on the real-world Cityscapes dataset.
Abstract (translated)
近年来,紧凑高效的场景理解表示在提高机器人系统的情境意识和自主能力方面越来越受欢迎。在这项工作中,我们介绍了PanopticEdge segmentation的概念,并提出PENet,一种 novel检测网络,它结合了语义边缘检测和实例级感知,形成了一个紧凑的PanopticEdgeRepresentation。通过共同的网络任务学习,concurrently预测语义边缘、实例中心和偏移流地图,而不需要边界框预测,利用了任务之间的跨任务correlations。我们提出的这种方法可以扩展语义边缘检测,使其涵盖了分类意识和实例意识分割。我们验证了所提出的PanopticEdge segmentation方法,并在现实城市景观数据集上展示了其有效性。
URL
https://arxiv.org/abs/2303.08848
