Learning Unified Representation of 3D Gaussian Splatting
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918316237389824 |
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| author | Xin, Yuelin Liu, Yuheng Xie, Xiaohui Li, Xinke |
| author_facet | Xin, Yuelin Liu, Yuheng Xie, Xiaohui Li, Xinke |
| contents | A well-designed vectorized representation is crucial for the learning systems natively based on 3D Gaussian Splatting. While 3DGS enables efficient and explicit 3D reconstruction, its parameter-based representation remains hard to learn as features, especially for neural-network-based models. Directly feeding raw Gaussian parameters into learning frameworks fails to address the non-unique and heterogeneous nature of the Gaussian parameterization, yielding highly data-dependent models. This challenge motivates us to explore a more principled approach to represent 3D Gaussian Splatting in neural networks that preserves the underlying color and geometric structure while enforcing unique mapping and channel homogeneity. In this paper, we propose an embedding representation of 3DGS based on continuous submanifold fields that encapsulate the intrinsic information of Gaussian primitives, thereby benefiting the learning of 3DGS. Implementation available at https://github.com/cilix-ai/gs-embedding |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_22917 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Unified Representation of 3D Gaussian Splatting Xin, Yuelin Liu, Yuheng Xie, Xiaohui Li, Xinke Computer Vision and Pattern Recognition I.4.10 A well-designed vectorized representation is crucial for the learning systems natively based on 3D Gaussian Splatting. While 3DGS enables efficient and explicit 3D reconstruction, its parameter-based representation remains hard to learn as features, especially for neural-network-based models. Directly feeding raw Gaussian parameters into learning frameworks fails to address the non-unique and heterogeneous nature of the Gaussian parameterization, yielding highly data-dependent models. This challenge motivates us to explore a more principled approach to represent 3D Gaussian Splatting in neural networks that preserves the underlying color and geometric structure while enforcing unique mapping and channel homogeneity. In this paper, we propose an embedding representation of 3DGS based on continuous submanifold fields that encapsulate the intrinsic information of Gaussian primitives, thereby benefiting the learning of 3DGS. Implementation available at https://github.com/cilix-ai/gs-embedding |
| title | Learning Unified Representation of 3D Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition I.4.10 |
| url | https://arxiv.org/abs/2509.22917 |