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GrowSP++: Growing Superpoints and Primitives for Unsupervised 3D Semantic Segmentation.

Zihui Zhang, Weisheng Dai, Bing Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence

|January 2, 2026

Summary

GrowSP++ is an unsupervised method for 3D semantic segmentation of point clouds. It achieves state-of-the-art performance without human labels by progressively growing superpoints and semantic primitives.

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Area of Science:

Computer Vision
Machine Learning
3D Data Analysis

Background:

3D semantic segmentation is crucial for understanding complex scenes.
Existing methods heavily rely on extensive human annotations, limiting scalability.
Unsupervised approaches are needed to overcome annotation bottlenecks.

Purpose of the Study:

To introduce GrowSP++, an unsupervised method for 3D semantic segmentation.
To eliminate the need for human labels in training neural networks for point cloud analysis.
To enable accurate identification of semantic classes in 3D scenes without supervision.

Main Methods:

GrowSP++ utilizes a feature extractor with 2D-3D feature distillation.
A superpoint constructor employs progressively growing superpoints.
A semantic primitive constructor incorporates an additional growing strategy.

Main Results:

GrowSP++ demonstrates state-of-the-art performance across five challenging indoor and outdoor datasets.
The method successfully segments complex semantic classes in raw point clouds.
Unsupervised learning of 3D semantic features is achieved without human annotations.

Conclusions:

GrowSP++ offers a novel unsupervised approach for 3D semantic segmentation.
The progressive growing strategy is key to learning semantic features effectively.
This work paves the way for advanced unsupervised 3D semantic learning methods.