  Existing point cloud semantic segmentation networks cannot identify unknown
classes and update their knowledge, due to a closed-set and static perspective
of the real world, which would induce the intelligent agent to make bad
decisions. To address this problem, we propose a Probability-Driven Framework
(PDF) for open world semantic segmentation that includes (i) a lightweight
U-decoder branch to identify unknown classes by estimating the uncertainties,
(ii) a flexible pseudo-labeling scheme to supply geometry features along with
probability distribution features of unknown classes by generating pseudo
labels, and (iii) an incremental knowledge distillation strategy to incorporate
novel classes into the existing knowledge base gradually. Our framework enables
the model to behave like human beings, which could recognize unknown objects
and incrementally learn them with the corresponding knowledge. Experimental
results on the S3DIS and ScanNetv2 datasets demonstrate that the proposed PDF
outperforms other methods by a large margin in both important tasks of open
world semantic segmentation.
