Jian Xu, Yi Li, Ying Huang, Siyi Wu, Bingzhe Li, Yuede Ji, Miao Yin · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30013 · open-access PDF
Bahasa 3D Gaussian Splatting memungkinkan pemahaman adegan kata terbuka dengan mengaitkan primitif Gaussian dengan fitur bahasa. Atribut semantik ini meningkatkan penyimpanan model, sementara sebagian besar metode kompresi 3DGS yang ada dirancang terutama untuk mempertahankan kualitas rendering RGB. Satu pendekatan sederhana adalah menerapkan sparsifikasi dan kuantifikasi secara berurutan. Namun, pemotongan berdasarkan kualitas rendering hanya dapat menghilangkan Gaussians yang memiliki kontribusi RGB kecil tetapi penting untuk prediksi semantik.
Machine translation (Meta M2M100); the English original follows.
Language 3D Gaussian Splatting enables open-vocabulary scene understanding by associating Gaussian primitives with language features. These semantic attributes increase model storage, while most existing 3DGS compression methods are designed mainly to preserve RGB rendering quality. One straightforward approach is to apply sparsification and quantization sequentially. However, pruning based only on rendering quality can remove Gaussians that have small RGB contributions but are important for semantic prediction. Once removed, their semantic information cannot be recovered by later quantization
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