Generative Data Augmentation for Object Point Cloud Segmentation


Dekai Zhu (Technische Universität München), Stefan Gavranovic (Siemens AG), Flavien Boussuge (Siemens AG), Benjamin Busam (Technische Universität München), Slobodan Ilic (Technische Universität München)
The 35th British Machine Vision Conference
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Abstract

Data augmentation is widely used to train deep learning models to address data scarcity. However, traditional data augmentation (TDA) typically relies on simple geometric transformation, such as random rotation and rescaling, resulting in minimal data diversity enrichment and limited model performance improvement. State-of-the-art generative models for 3D shape generation rely on the denoising diffusion probabilistic models and manage to generate realistic novel point clouds for 3D content creation and manipulation. Nevertheless, the generated 3D shapes lack associated point-wise semantic labels, restricting their usage in enlarging the training data for point cloud segmentation tasks. To bridge the gap between data augmentation techniques and the advanced diffusion models, we extend the state-of-the-art 3D diffusion model, Lion, to a part-aware generative model that can generate high-quality point clouds conditioned on given segmentation masks. Leveraging the novel generative model, we introduce a 3-step generative data augmentation (GDA) pipeline for point cloud segmentation training. Our GDA approach requires only a small amount of labeled samples but enriches the training data with generated variants and pseudo-labeled samples, which are validated by a novel diffusion-based pseudo-label filtering method. Extensive experiments on two large-scale synthetic datasets and a real-world medical dataset demonstrate that our GDA method outperforms TDA approach and related semi-supervised and self-supervised methods.

Citation

@inproceedings{Zhu_2025_BMVC,
author    = {Dekai Zhu and Stefan Gavranovic and Flavien Boussuge and Benjamin Busam and Slobodan Ilic},
title     = {Generative Data Augmentation for Object Point Cloud Segmentation},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_1037/paper.pdf}
}


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