论文标题
LIDAR点云的开放世界语义细分
Open-world Semantic Segmentation for LIDAR Point Clouds
论文作者
论文摘要
激光雷达语义分割的当前方法对于现实世界应用,例如自动驾驶,因为它是封闭式且静态的,因此不够强大。封闭设置的假设使网络只能输出训练的类的标签,即使是从未见过的对象,而静态网络也无法根据所见的知识来更新其知识库。因此,在这项工作中,我们建议对LiDAR Point Clouds的开放世界语义分割任务,其目的是1)使用开放式语义分段确定旧类和新颖的类,以及2)逐渐将新颖的对象逐渐将新颖的对象纳入现有的知识库中,而无需忘记旧类。为此,我们提出了一个冗余分类器(真实)框架,为开放式语义细分和增量学习问题提供一般体系结构。实验结果表明,在Semantickitti和Nuscenes数据集中,REAL可以同时实现开放式语义分割任务中的最新性能,并在增量学习过程中减轻灾难性遗忘问题,并以较大的利润率。
Current methods for LIDAR semantic segmentation are not robust enough for real-world applications, e.g., autonomous driving, since it is closed-set and static. The closed-set assumption makes the network only able to output labels of trained classes, even for objects never seen before, while a static network cannot update its knowledge base according to what it has seen. Therefore, in this work, we propose the open-world semantic segmentation task for LIDAR point clouds, which aims to 1) identify both old and novel classes using open-set semantic segmentation, and 2) gradually incorporate novel objects into the existing knowledge base using incremental learning without forgetting old classes. For this purpose, we propose a REdundAncy cLassifier (REAL) framework to provide a general architecture for both the open-set semantic segmentation and incremental learning problems. The experimental results show that REAL can simultaneously achieves state-of-the-art performance in the open-set semantic segmentation task on the SemanticKITTI and nuScenes datasets, and alleviate the catastrophic forgetting problem with a large margin during incremental learning.