Precision Oral Point Cloud Abnormal Region Segmentation via Embedded Feature Optimization

LI Letian; CHEN Sheng

doi:10.3969/j.issn.1000-1158.2025.05.04

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Acta Metrologica Sinica ›› 2025, Vol. 46 ›› Issue (5) : 644-650. DOI: 10.3969/j.issn.1000-1158.2025.05.04

Precision Oral Point Cloud Abnormal Region Segmentation via Embedded Feature Optimization

LI Letian ,
CHEN Sheng

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Abstract

A method for separating abnormal regions in oral point clouds based on embedded feature optimization is proposed， where abnormal parts of each point cloud are removed while accurate parts are preserved. Firstly， 2000 oral point cloud datasets are collected， and the abnormal regions to be removed are labeled， creating datasets for training， validation， and testing. Subsequently， an iterative optimization approach based on the Expectation-Maximization （EM） algorithm and the concept of contrastive learning is employed to enhance feature optimization in a point cloud denoising model， with training conducted over 200 iterations and results validated on the test set. Finally， quantitative analysis based on mean Intersection over union R _mIoUand qualitative analysis based on point cloud visualization are used as evaluation metrics to compare the proposed method with several common point cloud segmentation and denoising models. The results demonstrate that the R _mIoU of the method improved by 1.08% compared to other approaches， indicating superior performance in separating abnormal regions in oral point clouds.

Key words

machine vision measurement / intraoral point cloud / point cloud denoising / point cloud segmentation / digital dentistry / feature optimization

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LI Letian , CHEN Sheng. Precision Oral Point Cloud Abnormal Region Segmentation via Embedded Feature Optimization[J]. Acta Metrologica Sinica. 2025, 46(5): 644-650 https://doi.org/10.3969/j.issn.1000-1158.2025.05.04

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