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.

该研究提出了一种基于 LIDAR 点云数据的开放式语义分割任务，使用 REdundAncy cLassifier 框架解决开放式语义分割和增量学习问题，并在 SemanticKITTI 和 nuScenes 数据集上达到了最先进的性能，同步解决了灾难性遗忘问题。