G2P: Gaussian-to-Point Attribute Alignment for Boundary-Aware 3D Semantic Segmentation
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915783598145536 |
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| author | Song, Hojun Song, Chae-yeong Hong, Jeong-hun Moon, Chaewon Kim, Dong-hwi Kim, Gahyeon Kim, Soo Ye Liao, Yiyi Lee, Jaehyup Park, Sang-hyo |
| author_facet | Song, Hojun Song, Chae-yeong Hong, Jeong-hun Moon, Chaewon Kim, Dong-hwi Kim, Gahyeon Kim, Soo Ye Liao, Yiyi Lee, Jaehyup Park, Sang-hyo |
| contents | Semantic segmentation on point clouds is critical for 3D scene understanding. However, sparse and irregular point distributions provide limited appearance evidence, making geometry-only features insufficient to distinguish objects with similar shapes but distinct appearances (e.g., color, texture, material). We propose Gaussian-to-Point (G2P), which transfers appearance-aware attributes from 3D Gaussian Splatting to point clouds for more discriminative and appearance-consistent segmentation. Our G2P address the misalignment between optimized Gaussians and original point geometry by establishing point-wise correspondences. By leveraging Gaussian opacity attributes, we resolve the geometric ambiguity that limits existing models. Additionally, Gaussian scale attributes enable precise boundary localization in complex 3D scenes. Extensive experiments demonstrate that our approach achieves superior performance on standard benchmarks and shows significant improvements on geometrically challenging classes, all without any 2D or language supervision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03510 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | G2P: Gaussian-to-Point Attribute Alignment for Boundary-Aware 3D Semantic Segmentation Song, Hojun Song, Chae-yeong Hong, Jeong-hun Moon, Chaewon Kim, Dong-hwi Kim, Gahyeon Kim, Soo Ye Liao, Yiyi Lee, Jaehyup Park, Sang-hyo Computer Vision and Pattern Recognition Semantic segmentation on point clouds is critical for 3D scene understanding. However, sparse and irregular point distributions provide limited appearance evidence, making geometry-only features insufficient to distinguish objects with similar shapes but distinct appearances (e.g., color, texture, material). We propose Gaussian-to-Point (G2P), which transfers appearance-aware attributes from 3D Gaussian Splatting to point clouds for more discriminative and appearance-consistent segmentation. Our G2P address the misalignment between optimized Gaussians and original point geometry by establishing point-wise correspondences. By leveraging Gaussian opacity attributes, we resolve the geometric ambiguity that limits existing models. Additionally, Gaussian scale attributes enable precise boundary localization in complex 3D scenes. Extensive experiments demonstrate that our approach achieves superior performance on standard benchmarks and shows significant improvements on geometrically challenging classes, all without any 2D or language supervision. |
| title | G2P: Gaussian-to-Point Attribute Alignment for Boundary-Aware 3D Semantic Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.03510 |