视觉感知的投影式三维语义分割后处理方法

doi:10.3969/j.issn.0255-8297.2026.04.008

Abstract

Abstract: Projection-based three-dimensional semantic segmentation methods can effectively reduce the processing complexity and computational cost of three-dimensional data. However, most existing methods rely on the RGB color space for feature extraction, making them susceptible to illumination variations, shadow interference, and category color similarity, which limits segmentation accuracy. To address these issues, inspired by the color perception mechanism of the human visual system, this paper proposed a projection-based three-dimensional semantic segmentation method based on multi-color space post-processing. First, the three-dimensional scene was transformed into a regular two-dimensional image through two-dimensional projection and rasterization, and an initial segmentation result was obtained using a deep learning model. Second, in low-confidence regions, Lab and HSV color space features that are more consistent with human visual perception characteristics were introduced for clustering optimization to improve category separability and regional consistency. Third, combined with the two-dimensional–threedimensional mapping relationship, the optimized semantic labels were restored to the threedimensional scene, achieving high-precision semantic annotation. The experimental results show that the proposed method achieves good segmentation performance on four sets of complex urban scene data. Compared with traditional clustering methods, the overall accuracy(OA) is improved by approximately 21% on average, and the Kappa coefficient is increased by approximately 0.28 on average. Compared with deep learning-only methods,the OA is improved by approximately 5% on average, and the Kappa coefficient is increased by approximately 0.07 on average. The results indicate that the proposed method can effectively enhance the accuracy and stability of three-dimensional semantic segmentation in complex scenes.

Key words: three-dimensional semantic segmentation, projection-based method, postprocessing optimization, deep learning

CLC Number:

P237

ZOU Guojing, CONG Ming, CUI Jianjun, HAN Ling. Visual Perception-Based Post-Processing Method for Projection-Based Three-Dimensional Semantic Segmentation[J]. Journal of Applied Sciences, 2026, 44(4): 626-643.

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URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2026.04.008

https://www.jas.shu.edu.cn/EN/Y2026/V44/I4/626

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Visual Perception-Based Post-Processing Method for Projection-Based Three-Dimensional Semantic Segmentation

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