Mix3D data augmentation enhanced RandLA-Net for large-scale point cloud semantic segmentation

Junfeng Ding; Shengchao Guo; Mingyuan Li; Jian Zhou; Xuan Chen; Lei Chen

doi:10.1117/12.2675112

25 May 2023 Mix3D data augmentation enhanced RandLA-Net for large-scale point cloud semantic segmentation

Junfeng Ding, Shengchao Guo, Mingyuan Li, Jian Zhou, Xuan Chen, Lei Chen

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263606 (2023) https://doi.org/10.1117/12.2675112
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

3D point cloud semantic segmentation is one of the key technologies for fast 3D modeling of digital twins. To address the overfitting problem of the large-scale semantic segmentation model RandLA-Net, this paper proposes an improved RandLA-Net method based on Mix3D data augmentation. RandLA-Net is a high-volume, large-perception field model that can directly capture the contextual information of the entire 3D scene, while 3D datasets tend to be more expensive due to data sampling and labeling, often the number of scenes is small and the variance within the data is small, RandLA-Net can easily learn the overly strong contextual prior on the training set, and the model may show poor generalization ability when reasoning in realistic scenes. By introducing Mix3D to mix the two scenes to generate new training samples and implicitly place the object instances in the new contextual environment, the RandLA-Net model no longer relies solely on the scene context to infer semantic labels, but instead infer from the local structure, balancing the role of global context and local structure information in model inference and effectively reducing the overfitting of the training set context. The overfitting of the training set context is effectively reduced. Experimental results on several datasets show that our approach results in a 1.3% and 0.6% mIoU improvement of the RandLA-Net model on Semantic3D and S3DIS datasets.

Citation Download Citation

Junfeng Ding, Shengchao Guo, Mingyuan Li, Jian Zhou, Xuan Chen, and Lei Chen "Mix3D data augmentation enhanced RandLA-Net for large-scale point cloud semantic segmentation", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263606 (25 May 2023); https://doi.org/10.1117/12.2675112

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KEYWORDS

Point clouds

Data modeling

3D modeling

Image segmentation

Overfitting

Convolution

RGB color model

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