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| Title | Demf-Net: Dual-Branch Feature Enhancement And Multi-Scale Fusion For Semantic Segmentation Of Large-Scale Point Clouds; [Demf-Net：基于双分支增强和多尺度融合的大规模点云语义分割] |
|---|---|
| ID_Doc | 18256 |
| Authors | Li Z.; Ning X.; Lv Z.; Shi Z.; Jin H.; Wang Y.; Zhou W. |
| Year | 2025 |
| Published | Journal of Graphics, 46, 2 |
| DOI | http://dx.doi.org/10.11996/JG.j.2095-302X.2025020259 |
| Abstract | Large-scale point cloud semantic segmentation serves as a critical task in the domain of 3D vision, with broad applications across fields such as autonomous driving, robotic navigation, smart city construction, and virtual reality. However, existing methods relying on down-sampling and exhibiting excessive disparities between multi-scale features often suffer from a substantial loss in the ability to capture fine-grained details and local structures. This degradation in the model’s capacity to preserve such local features impairs the accuracy of semantic segmentation. To address these issues, a novel semantic segmentation framework, DEMF-Net was proposed, which integrated dual-branch feature enhancement and multi-scale fusion strategies. The network incorporated a dual-branch enhanced aggregation module, which was designed to jointly encode point cloud attribute information and semantic features from the local neighborhood. Bilateral features were leveraged and embedded into corresponding original features, thereby improving the model’s ability to capture local details with higher fidelity. Furthermore, a multi-scale feature fusion module was introduced to effectively reduce the semantic gap between features at different scales. This module facilitated the fusion of adjacent multi-scale features, resulting in a global feature representation that synthesized information across all encoding layers. Such a design significantly enhanced the model’s global context awareness and enabled the integration of both upper and lower layer encoding, thereby enhancing the feature recognition capabilities. Comprehensive experiments were conducted on two widely used point cloud datasets, SensatUrban and S3DIS, to validate the performance of the proposed approach. Experimental results demonstrated that the mean Intersection over Union (mIoU) could be achieved by DEMF-Net at 61.6% and 66.7%, respectively, outperforming existing state-of-the-art methods. © 2025 Editorial of Board of Journal of Graphics. All rights reserved. |
| Author Keywords | feature encoding; large-scale point cloud; semantic segmentation; three-dimensional vision; urban scene |
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