论文标题
高效：有效的全景分割
EfficientPS: Efficient Panoptic Segmentation
论文作者
论文摘要
了解自主机器人操作的场景对于其胜任的功能至关重要。这样的场景理解需要识别流量参与者的实例以及一般场景语义，这些语义可以通过全景分割任务有效地解决。在本文中，我们介绍了由共享骨干组成的有效的圆形分割（有效PS）体系结构，该骨架有效地编码和融合了语义上丰富的多尺度功能。我们结合了一个新的语义头，该语义头将良好和上下文的特征相干地汇总，并将Mask R-CNN的新变体作为实例头。我们还提出了一个新颖的圆锥融合模块，该模块可一致地整合了来自高效PPS体系结构的两个头部的输出逻辑，以产生最终的综合分割输出。此外，我们介绍了Kitti Panoptic分割数据集，该数据集包含了普遍具有挑战性的Kitti基准测试的全面注释。对城市景观，Kitti，Mapillary Vistas和Indian Driving Dataset的广泛评估表明，我们提出的架构始终在这四个基准上设置新的最新技术，同时是迄今为止最有效，最快速的泛型细分架构。
Understanding the scene in which an autonomous robot operates is critical for its competent functioning. Such scene comprehension necessitates recognizing instances of traffic participants along with general scene semantics which can be effectively addressed by the panoptic segmentation task. In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and fuses semantically rich multi-scale features. We incorporate a new semantic head that aggregates fine and contextual features coherently and a new variant of Mask R-CNN as the instance head. We also propose a novel panoptic fusion module that congruously integrates the output logits from both the heads of our EfficientPS architecture to yield the final panoptic segmentation output. Additionally, we introduce the KITTI panoptic segmentation dataset that contains panoptic annotations for the popularly challenging KITTI benchmark. Extensive evaluations on Cityscapes, KITTI, Mapillary Vistas and Indian Driving Dataset demonstrate that our proposed architecture consistently sets the new state-of-the-art on all these four benchmarks while being the most efficient and fast panoptic segmentation architecture to date.
