AI RESEARCH
NRGS: Neural Regularization for Robust 3D Semantic Gaussian Splatting
arXiv CS.CV
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ArXi:2604.22439v1 Announce Type: new We propose a neural regularization method that refines the noisy 3D semantic field produced by lifting multi-view inconsistent 2D features, in order to obtain an accurate and robust 3D semantic Gaussian Splatting. The 2D features extracted from vision foundation models suffer from multi-view inconsistency due to a lack of cross-view constraints. Lifting these inconsistent features directly into 3D Gaussians results in a noisy semantic field, which degrades the performance of downstream tasks.