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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2605.08293 |
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| _version_ | 1866911679030231040 |
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| author | Wang, Yijing Li, Ruonan Wang, Qilin Zhao, Rongqiang Liu, Jie |
| author_facet | Wang, Yijing Li, Ruonan Wang, Qilin Zhao, Rongqiang Liu, Jie |
| contents | 3D semantic scene understanding is essential for digital twins, autonomous driving, smart agriculture, and embodied perception, yet dense point-wise annotation for point clouds remains expensive and difficult to scale. Existing annotation-free methods often face a trade-off between semantic recognition and structural efficiency: open-vocabulary and foundation-model-driven methods provide strong semantic priors, but often come with substantial computational costs, while structure-oriented methods based on superpoints, clustering, and graph reasoning are lightweight but often produce category-agnostic regions. We propose DDS, a resource-efficient structure-oriented framework for region-consistent and semanticized annotation-free 3D scene understanding. DDS preserves the lightweight superpoint-based organization paradigm while incorporating visual semantic cues from projected features and segmentation-derived masks. It first performs multi-granularity distillation to guide the 3D backbone at the point, mask-prototype, and inter-prototype levels, then applies graph diffusion over superpoints to propagate semantic information directly in 3D, producing coherent region representations without costly spectral decomposition or dense open-vocabulary 3D feature fields. Finally, DDS uses segmentation-cluster association to assign interpretable semantic names to category-agnostic 3D clusters. Experiments on real-world datasets show that DDS achieves the best performance among representative structure-oriented annotation-free baselines, improving oAcc, mAcc, and mIoU by up to 5.9%, 8.1%, and 2.4%, respectively. These results demonstrate that DDS improves region consistency and lightweight semantic recognition, providing a scalable and interpretable solution for annotation-free 3D scene understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08293 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Distill, Diffuse, and Semanticize (DDS): Annotation-Free 3D Scene Understanding Based on Multi-Granularity Distillation and Graph-Diffusion-Based Segmentation Wang, Yijing Li, Ruonan Wang, Qilin Zhao, Rongqiang Liu, Jie Computer Vision and Pattern Recognition 3D semantic scene understanding is essential for digital twins, autonomous driving, smart agriculture, and embodied perception, yet dense point-wise annotation for point clouds remains expensive and difficult to scale. Existing annotation-free methods often face a trade-off between semantic recognition and structural efficiency: open-vocabulary and foundation-model-driven methods provide strong semantic priors, but often come with substantial computational costs, while structure-oriented methods based on superpoints, clustering, and graph reasoning are lightweight but often produce category-agnostic regions. We propose DDS, a resource-efficient structure-oriented framework for region-consistent and semanticized annotation-free 3D scene understanding. DDS preserves the lightweight superpoint-based organization paradigm while incorporating visual semantic cues from projected features and segmentation-derived masks. It first performs multi-granularity distillation to guide the 3D backbone at the point, mask-prototype, and inter-prototype levels, then applies graph diffusion over superpoints to propagate semantic information directly in 3D, producing coherent region representations without costly spectral decomposition or dense open-vocabulary 3D feature fields. Finally, DDS uses segmentation-cluster association to assign interpretable semantic names to category-agnostic 3D clusters. Experiments on real-world datasets show that DDS achieves the best performance among representative structure-oriented annotation-free baselines, improving oAcc, mAcc, and mIoU by up to 5.9%, 8.1%, and 2.4%, respectively. These results demonstrate that DDS improves region consistency and lightweight semantic recognition, providing a scalable and interpretable solution for annotation-free 3D scene understanding. |
| title | Distill, Diffuse, and Semanticize (DDS): Annotation-Free 3D Scene Understanding Based on Multi-Granularity Distillation and Graph-Diffusion-Based Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.08293 |