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Novel Class Discovery for 3D Point Cloud Semantic Segmentation

2023-03-21 06:10:39
Luigi Riz, Cristiano Saltori, Elisa Ricci, Fabio Poiesi


Novel class discovery (NCD) for semantic segmentation is the task of learning a model that can segment unlabelled (novel) classes using only the supervision from labelled (base) classes. This problem has recently been pioneered for 2D image data, but no work exists for 3D point cloud data. In fact, the assumptions made for 2D are loosely applicable to 3D in this case. This paper is presented to advance the state of the art on point cloud data analysis in four directions. Firstly, we address the new problem of NCD for point cloud semantic segmentation. Secondly, we show that the transposition of the only existing NCD method for 2D semantic segmentation to 3D data is suboptimal. Thirdly, we present a new method for NCD based on online clustering that exploits uncertainty quantification to produce prototypes for pseudo-labelling the points of the novel classes. Lastly, we introduce a new evaluation protocol to assess the performance of NCD for point cloud semantic segmentation. We thoroughly evaluate our method on SemanticKITTI and SemanticPOSS datasets, showing that it can significantly outperform the baseline. Project page at this link: this https URL.

Abstract (translated)

新的类发现(NCD)语义分割任务的任务是学习一种模型,可以利用标记(基础)类的监督来分割未标记(新)类。这个问题最近在2D图像数据上率先提出,但对于3D点云数据却没有研究。事实上,对于2D的假设在此处并不适用于3D。本文旨在推进点云数据分析的前沿技术,从四个方面推进。首先,我们解决点云语义分割中的NCD新问题。其次,我们表明将仅使用2D语义分割方法中的唯一可用NCD方法应用于3D数据是性能较差的。第三,我们提出了基于在线聚类的NCD新方法,利用不确定性量化生产原型,用于伪标记新类点的原型。最后,我们引入了一种新的评估协议,以评估点云语义分割中的NCD性能。我们对SemanticKITTI和SemanticPOSS数据集进行了充分的评估,表明它可以显著优于基准。该项目页面在此链接:此https URL。



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