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CLIP-GS: CLIP-Informed Gaussian Splatting for View-Consistent 3D Indoor Semantic Understanding

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arxiv 2404.14249 v2 pith:7NEGTMP7 submitted 2024-04-22 cs.CV

CLIP-GS: CLIP-Informed Gaussian Splatting for View-Consistent 3D Indoor Semantic Understanding

Guibiao Liao , Jiankun Li , Zhenyu Bao , Xiaoqing Ye , Qing Li , Kanglin Liu This is my paper
classification cs.CV
keywords semanticgaussianclipcoherentindoorunderstandingachievingapproach
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Exploiting 3D Gaussian Splatting (3DGS) with Contrastive Language-Image Pre-Training (CLIP) models for open-vocabulary 3D semantic understanding of indoor scenes has emerged as an attractive research focus. Existing methods typically attach high-dimensional CLIP semantic embeddings to 3D Gaussians and leverage view-inconsistent 2D CLIP semantics as Gaussian supervision, resulting in efficiency bottlenecks and deficient 3D semantic consistency. To address these challenges, we present CLIP-GS, efficiently achieving a coherent semantic understanding of 3D indoor scenes via the proposed Semantic Attribute Compactness (SAC) and 3D Coherent Regularization (3DCR). SAC approach exploits the naturally unified semantics within objects to learn compact, yet effective, semantic Gaussian representations, enabling highly efficient rendering (>100 FPS). 3DCR enforces semantic consistency in 2D and 3D domains: In 2D, 3DCR utilizes refined view-consistent semantic outcomes derived from 3DGS to establish cross-view coherence constraints; in 3D, 3DCR encourages features similar among 3D Gaussian primitives associated with the same object, leading to more precise and coherent segmentation results. Extensive experimental results demonstrate that our method remarkably suppresses existing state-of-the-art approaches, achieving mIoU improvements of 21.20% and 13.05% on ScanNet and Replica datasets, respectively, while maintaining real-time rendering speed. Furthermore, our approach exhibits superior performance even with sparse input data, substantiating its robustness.

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Cited by 7 Pith papers

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    Ground-truth boxes, when used as a privileged visual hint for a frozen teacher during training, improve autoregressive visual grounding in multimodal LLMs by about 4 Acc@0.7 points with no inference-time overhead.

  4. IoU-PD: IoU-Aware Privileged Distillation for Visual Grounding with Multimodal Large Language Models

    cs.CV 2026-07 conditional novelty 5.0

    Training a coordinate-generating VLM with an IoU-aware distillation loss from a teacher that sees the ground-truth box marked on the image improves referring-expression grounding by ~3-4 accuracy points.

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  6. LIVE-GS: LLM Powers Interactive VR Experience with Physics-Aware Gaussian Splatting

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