SeG-Gaussian: segmentation-guided 3D Gaussian optimisation for novel view synthesis

Zhang, Ling-Xiao, Jiang, Chenbo, Lai, YuKun ORCID: https://orcid.org/0000-0002-2094-5680 and Gao, Lin 2025. SeG-Gaussian: segmentation-guided 3D Gaussian optimisation for novel view synthesis. IEEE Transactions on Visualization and Computer Graphics , pp. 1-13. 10.1109/tvcg.2025.3615421

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Abstract

Radiance field based methods have recently revolutionized novel view synthesis of scenes captured with multi-view photos. A significant recent advance is 3D Gaussian Splatting (3DGS), which utilizes a set of 3D Gaussians to represent a radiance field, yielding high-fidelity results in real-time rendering. However, we have observed that 3DGS struggles to capture the necessary details in sparsely observed regions, where there is not enough gradient for effective split and clone operations. In this paper, we present a novel solution to address this limitation. Our key idea is to leverage segmentation information to identify poorly optimized regions within the 3D Gaussian representation. By applying split or clone operations on the corresponding 3D Gaussians in these regions, we aim to refine the spatial distribution of Gaussians and enhance the overall quality of high-fidelity 3D scene reconstruction. To further optimize the reconstruction process, we introduce two spatial regularization terms: repulsion loss and smoothness loss. These terms effectively minimize overlap and redundancy among Gaussians, reducing outliers in the synthesized geometry. By incorporating these regularization techniques, our approach achieves state-of-the-art performance in real-time novel view synthesis and significantly improves visibility in less observed regions, leading to a more compact and accurate 3D scene representation.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html, Start Date: 2025-01-01
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1077-2626
Last Modified:	17 Oct 2025 10:15
URI:	https://orca.cardiff.ac.uk/id/eprint/181732

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