论文标题
全景景观分割：通过无监督的对比学习的洞察移动代理的解析
Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive Learning
论文作者
论文摘要
在这项工作中，我们在视野（FOV）（FOV）和图像级别的理解方面介绍了全景景观分割，作为基于标准摄像头的输入的图像级别的理解。完整的环境理解为移动代理提供了最大信息。这是任何智能车辆在安全至关重要的动态环境（例如现实世界流量）中做出明智决定的重要信息。为了克服缺乏带注释的全景图像，我们提出了一个框架，该框架允许对标准针孔图像进行模型训练，并以成本限制的方式将学习的功能传输到全景域。从针孔到全景图像的域移动是非平凡的，因为大物体和表面靠近图像边框区域，并且在两个域上看起来不同。使用我们提出的方法和密集的对比度学习，我们设法对非适应方法实现了重大改进。根据有效的综合分割体系结构，我们可以改善在我们既定的野生全景全景分割（WILDPPS）数据集中，以圆锥质量（PQ）测得的3.5-6.5％。此外，我们的有效框架不需要访问目标域的图像，这使其成为适合有限硬件设置的可行域概括方法。作为其他贡献，我们发布了WILDPPS：第一个全景全景图像数据集，以促进周围感知的进展，并探索一种结合了监督和对比培训的新型培训程序。
In this work, we introduce panoramic panoptic segmentation, as the most holistic scene understanding, both in terms of Field of View (FoV) and image-level understanding for standard camera-based input. A complete surrounding understanding provides a maximum of information to a mobile agent. This is essential information for any intelligent vehicle to make informed decisions in a safety-critical dynamic environment such as real-world traffic. In order to overcome the lack of annotated panoramic images, we propose a framework which allows model training on standard pinhole images and transfers the learned features to the panoramic domain in a cost-minimizing way. The domain shift from pinhole to panoramic images is non-trivial as large objects and surfaces are heavily distorted close to the image border regions and look different across the two domains. Using our proposed method with dense contrastive learning, we manage to achieve significant improvements over a non-adapted approach. Depending on the efficient panoptic segmentation architecture, we can improve 3.5-6.5% measured in Panoptic Quality (PQ) over non-adapted models on our established Wild Panoramic Panoptic Segmentation (WildPPS) dataset. Furthermore, our efficient framework does not need access to the images of the target domain, making it a feasible domain generalization approach suitable for a limited hardware setting. As additional contributions, we publish WildPPS: The first panoramic panoptic image dataset to foster progress in surrounding perception and explore a novel training procedure combining supervised and contrastive training.
