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
언어 3D Gaussian Splatting은 Gaussian 원시를 언어 특성과 연관함으로써 오픈 사전 장면 이해를 가능하게합니다.이 정의적 특성은 모델 저장량을 증가시키고, 대부분의 기존의 3DGS 압축 방법은 주로 RGB 렌더링 품질을 보존하도록 설계되었습니다. 하나의 간단한 접근법은 분열화와 정량화를 연속적으로 적용하는 것입니다.그러나 렌더링 품질에만 기초한 절단은 작은 RGB 기여를 가지고 있지만 정의적 예측에 중요한 Gaussians를 제거 할 수 있습니다.
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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