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ReferSplat: Referring Segmentation in 3D Gaussian Splatting
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ReferSplat: Referring Segmentation in 3D Gaussian Splatting
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We introduce Referring 3D Gaussian Splatting Segmentation (R3DGS), a new task that aims to segment target objects in a 3D Gaussian scene based on natural language descriptions, which often contain spatial relationships or object attributes. This task requires the model to identify newly described objects that may be occluded or not directly visible in a novel view, posing a significant challenge for 3D multi-modal understanding. Developing this capability is crucial for advancing embodied AI. To support research in this area, we construct the first R3DGS dataset, Ref-LERF. Our analysis reveals that 3D multi-modal understanding and spatial relationship modeling are key challenges for R3DGS. To address these challenges, we propose ReferSplat, a framework that explicitly models 3D Gaussian points with natural language expressions in a spatially aware paradigm. ReferSplat achieves state-of-the-art performance on both the newly proposed R3DGS task and 3D open-vocabulary segmentation benchmarks. Dataset and code are available at https://github.com/heshuting555/ReferSplat.
Forward citations
Cited by 8 Pith papers
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Open-Vocabulary and Referring Segmentation for 3D Gaussians Using 2D Detectors
GaussDet enables open-vocabulary and referring segmentation in 3D Gaussians by learning instance features and aggregating votes from 2D detectors, improving referential grounding by 16.7% mIoU in zero-shot setting.
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E3DGS: Unified Geometric-Photometric Equivariance for 3D Gaussian Splatting via Color-as-Geometry Embedding
3D Gaussian view-dependent colors are repacked as 3×3 matrices so geometry and color rotate together, giving exact rotation-equivariant recognition and world modeling in 3DGS.
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MV-GEL: Language-Driven Multi-View Geometric Entity Localization on Meshes
MV-GEL localizes fine-grained geometric entities on 3D meshes from natural language by ranking informative views with GELviews, applying VLM segmentation, and lifting masks via geometry-aware ray casting, reporting up...
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MV-GEL: Language-Driven Multi-View Geometric Entity Localization on Meshes
MV-GEL uses a learned view selector and a fine-tuned vision-language segmentation model to localize text-described faces and edges on 3D meshes.
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EnerGS: Energy-Based Gaussian Splatting with Partial Geometric Priors
EnerGS introduces an energy-based soft guidance mechanism for partial geometry in 3D Gaussian Splatting to improve reconstruction quality and reduce overfitting in sparse outdoor settings.
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PanopticQuery: Unified Query-Time Reasoning for 4D Scenes
PanopticQuery lifts 2D semantic predictions into globally consistent 4D groundings via neural field optimization and sets new state-of-the-art results on complex language queries for attributes, actions, and interactions.
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TrianguLang: Geometry-Aware Semantic Consensus for Pose-Free 3D Localization
TrianguLang achieves state-of-the-art feed-forward text-guided 3D localization and segmentation by using predicted geometry to gate cross-view semantic correspondences without ground-truth poses.
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G2P: Gaussian-to-Point Attribute Alignment for Boundary-Aware 3D Segmentation
G2P transfers opacity and scale attributes from 3D Gaussian Splatting to point clouds, improving semantic segmentation by +1.4 mIoU over PT v3 on ScanNet v2 and +2.1 on ScanNet200.
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