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.

了解自主机器人的操作场景对于其胜任的功能至关重要。这种场景理解需要识别交通参与者的实例以及可以由全景分割任务有效解决的一般场景语义。在本文中，我们介绍了有效全景全景图（EfficientPS）体系结构，该体系结构由共享主干组成，该主干可以有效地编码和融合语义丰富的多尺度特征。我们并入了一个新的语义头，该词头将相关的精细特征和上下文特征进行了聚合，并将一个新的Mask R-CNN变体作为实例头。我们还提出了一种新颖的全景融合模块，该模块可将EfficientPS体系结构的两个前端的输出logit紧密集成在一起，以产生最终的全景分割输出。此外，我们介绍了KITTI全景分割数据集，其中包含针对颇具挑战性的KITTI基准的全景注释。对Cityscapes，KITTI，Mapillary Vistas和Indian Driving Dataset的广泛评估表明，我们提出的架构在这四个基准上始终设置了最新技术，同时是迄今为止最高效，最快的全景分割架构。