TL;DR
SARG-GS is proposed, a geometry-driven 3DGS framework tailored for sparse-view scenarios, comprising a Semantic Augmented Epipolar Fusion (SAEF) module and a Residual Guided Reprojection Compensation (RRC) module, which achieves superior structural completeness and rendering fidelity with as few as three input views.
A semantic-guided 3D Gaussian splatting (3DGS) framework tailored to sparse-view industrial reconstruction was introduced, enabling robust reconstruction from limited viewpoints and offers a practical geometric foundation for automated inspection and remote equipment monitoring.
Abstract. 3D Gaussian Splatting (3DGS) has emerged as a leading technique for novel view synthesis (NVS), yet its performance degrades drastically under sparse-view conditions. While existing methods have sought to address this by incorporating accurate 3D geometry via Multi-View Stereo (MVS) or LiDAR priors, the view-...
Experiments show that CoMVS-GS remains competitive on object-level reconstruction and improves geometric accuracy and mesh compactness in outdoor scenes while maintaining high rendering quality.
Results indicate that a direct, multi-attribute 3D consistency objective, when combined with high-quality correspondences, is effective for addressing the ill-posed sparse-view reconstruction problem.
The proposed approach achieves competitive or superior performance compared with 3DGS, SpotLessSplats, T-3DGS, and RobustSplat on standard image-quality metrics, including peak signal-to-noise ratio, structural similarity index measure, and learned perceptual image patch similarity.
This work aligns monocular estimates with valid multi-view geometric depths and verify their consistency to identify reliable geometric anchors, which support consistency-aware pruning and depth supervision, and aligned mono-only estimates and RGB-D joint optimization improves appearance fidelity and geometric consiste...
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