Point-level label-free segmentation framework for 3D point cloud semantic mining

Anan Du, Shuchao Pang*, Mehmet Orgun

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


3D point cloud data semantic mining plays a key role in 3D scene understanding. Although recent point cloud semantic mining methods have achieved great success, they require large amounts of expensive manual annotated data. More importantly, the lack of large-scale annotated datasets limits those approaches in many real-world applications, especially for point-level semantic mining tasks such as point cloud semantic segmentation. In this work, we propose a novel point cloud segmentation framework, called Point-level Label-free Segmentation framework (PLS), that does not require point-level annotations. In this framework, the point cloud semantic mining task is formulated as a clustering problem based on mutual information. Meanwhile, our method can directly predict clusters that correspond to the given semantic classes in a single feed-forward pass of a neural network. We apply the proposed PLS to the shape part segmentation task. Experiments on the benchmark ShapeNetPart dataset demonstrate that our method has the ability to discover clusters that match semantic classes, and it can produce comparable results with methods using incomplete labels on several categories.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications
Subtitle of host publication19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, proceedings, part I
EditorsXiaochun Yang, Heru Suhartanto, Guoren Wang, Bin Wang, Jing Jiang, Bing Li, Huaijie Zhu, Ningning Cui
Place of PublicationCham
PublisherSpringer, Springer Nature
Number of pages14
ISBN (Electronic)9783031466618
ISBN (Print)9783031466601
Publication statusPublished - 2023
Event19th International Conference on Advanced Data Mining and Applications, ADMA 2023 - Shenyang, China
Duration: 21 Aug 202323 Aug 2023

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Advanced Data Mining and Applications, ADMA 2023


  • Data mining
  • Semantic mining
  • 3D point cloud
  • Mutual information
  • Unsupervised clustering


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