Indoor point cloud segmentation algorithm based on improved RepSurf

Huahao Luo; Deli Zhu; Weili Liu

doi:10.1117/12.3035111

19 July 2024 Indoor point cloud segmentation algorithm based on improved RepSurf

Huahao Luo, Deli Zhu, Weili Liu

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131G (2024) https://doi.org/10.1117/12.3035111
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

To address the issue of insufficient extraction of local key features in existing point cloud semantic segmentation methods, we have made improvements to the RepSurf baseline model. Firstly, in the initial stages of the network architecture, we introduce a multi-layer perceptron to map the input point cloud to a higher dimension, thereby expanding the theoretical receptive field and matching the channel numbers between the encoder and decoder parts. Subsequently, we embed the InvResMLP into the Surface Set Abstraction Channel De-differentiation module to enhance the extraction capability of local features effectively while avoiding issues such as gradient vanishing and overfitting. Experimental results demonstrate the improved RepSurf, when applied in indoor scenarios with the S3DIS dataset as the segmentation target, achieved remarkable results. It achieved mIoU (mean Intersection over Union), mAcc (mean accuracy), and OA (overall accuracy) of 68.1%, 76.0%, and 89.6%, respectively. Compared to the baseline model, our approach exhibited significant improvements of 1.8%, 1.3%, and 0.6% in mIoU, mAcc, and OA, respectively.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huahao Luo, Deli Zhu, and Weili Liu "Indoor point cloud segmentation algorithm based on improved RepSurf", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131G (19 July 2024); https://doi.org/10.1117/12.3035111

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