Open-vocabulary referring segmentation in 3D Gaussian Splatting (3DGS) requires a neural model to select Gaussian primitives according to free-form language expressions. Existing 3DGS-based methods usually rely on global text-region similarity, which is weak for queries involving attributes, reference objects, spatial relations, and fine-grained parts. This often causes target-reference confusion, granularity mismatch, part-whole leakage, and relation violations. We propose QAGaussian, a query-adaptive neural reasoning framework for language-guided Gaussian primitive selection. QAGaussian first learns query-conditioned multi-scale Gaussian slots as differentiable candidates whose receptive fields are shaped by the input expression. It then builds a relation-aware slot graph with language-conditioned edge weighting to propagate target-reference, attribute, part-whole, and contextual evidence. A granularity-adaptive router softly combines region-level, object-level, part-level, attribute-aware, and relation-aware mask branches, followed by relation-constrained refinement for spatial, part-whole, attribute, and geometric consistency. QAGaussian is pretrained only on Mosaic3D-5.6M for Gaussian-text alignment and evaluated on independent benchmarks without target-dataset fine-tuning. It achieves 47.2 Avg. mIoU and 63.2 Avg. F1, outperforming the strongest 3DGS referring baseline by 2.7 mIoU points and 2.9 F1 points. It also improves Part-mIoU from 38.6 to 43.4, Rel-mIoU from 44.4 to 50.8, and reduces target-reference confusion from 10.8 to 7.4. These results demonstrate that query-conditioned slot learning, relation-aware graph reasoning, and adaptive routing provide an effective neural modeling strategy for open-vocabulary referring segmentation in 3DGS. The code is available at https://github.com/zqeslwyz/QAGaussian.
Figures & tables
Figure 1: Overall framework of QAGaussian. Given a 3D Gaussian Splatting scene and a free-form referring expression, the model generates query-conditioned multi-scale Gaussian slots, performs relation-aware slot reasoning, adaptively fuses multiple mask prediction branches, and refines the result to obtain the final Gaussian-level referring segmentation mask.
Item
Setting
Pretraining dataset
Mosaic3D-5.6M
Hidden dimension D
256
Gaussian tokenization scales
3
Slots per scale (K1,K2,K3)
96 / 64 / 32
Total slots K
192
Graph neighbors per slot
16
Table 1: Experimental settings and main hyperparameters.
Table 3: Average metrics on representative independent benchmarks
Figure 2: Qualitative comparison with CAGS and ReferSplat on representative 3DGS scenes. Each row shows the input scene, ground-truth mask, and segmentation results of different methods under the same referring expression. The colored query components are automatically identified by the language parser: red denotes the target object, blue denotes the spatial relation, brown denotes the reference object, and green denotes the attribute. QAGaussian produces cleaner Gaussian-level masks and better suppresses reference objects and distractors.
Figure 3: Visualization under different query types within the same complex kitchen 3DGS scene. The input scene is fixed, while the referring expression changes across object-level, attribute-aware, part-level, relation-dependent, and compositional queries. Each row shows Gaussian-level segmentation results from three views. The colored query components are automatically identified by the language parser: red denotes the target object, blue denotes the spatial relation, brown denotes the reference object, and green denotes the attribute.
Method
Part-mIoU
Rel-mIoU
TR-conf. ↓
Precision
Recall
F1
G-SAM + Fusion ( Ren et al., 2024b )
29.2
35.0
17.2
44.6
41.5
43.0
LISA + Fusion ( Lai et al., 2024a )
31.8
33.4
18.9
48.6
45.9
47.2
BUTD-DETR ( Jain et al., 2022a )
27.3
40.6
15.9
43.8
46.2
45.0
Multi3DRefer ( Zhang et al., 2023a )
29.2
41.8
15.6
45.4
48.0
46.7
Open3DIS ( Nguyen et al., 2024a )
33.5
39.6
14.8
49.2
50.4
49.8
LangSplat ( Qin et al., 2024a )
31.6
37.8
15.5
48.0
49.6
48.8
Table 4: Fine-grained diagnostic metrics on part-level queries from PartNet, PartNet-Mobility, and ShapeNetPart, and relation-dependent queries from ScanRefer, ReferIt3D, Multi3DRefer, and ReferSplat-derived splits. TR-conf. denotes the target-reference confusion rate.
Table 5: Effect of pretraining data on cross-dataset generalization. All settings are evaluated on independent benchmarks without target benchmark fine-tuning.
Figure 4: Performance retention under different query perturbations.
Table 6: Ablation study of key QAGaussian components on the independent evaluation benchmarks. TR-conf. denotes the target-reference confusion rate, where lower values are better.
Figure 5: Qualitative ablation example on a relation-dependent region query. The query components are automatically extracted by the language parser and visualized with different colors. The full QAGaussian model accurately segments the referred glass door, while removing query-conditioned slots, relation graph reasoning, or adaptive routing leads to coarse masks, activation of similar glass regions, or leakage to surrounding facade areas.
Method
Preprocess time
Query time
Memory
Avg. mIoU
Small
Medium
Large
Small
Medium
Large
Small
Medium
Large
OpenGaussian ( Wu et al., 2024a )
6.2m
8.6m
13.8m
0.28s
0.43s
0.68s
5.8GB
7.8GB
10.6GB
40.3
CAGS ( Sun et al., 2025a )
6.8m
9.4m
15.1m
0.36s
0.61s
0.92s
6.3GB
8.6GB
11.8GB
44.0
ReferSplat ( He et al., 2025a )
7.1m
9.8m
15.8m
0.40s
0.66s
1.05s
6.8GB
9.1GB
12.4GB
44.5
QAGaussian
7.4m
10.3m
16.6m
0.48s
0.82s
1.28s
7.5GB
9.8GB
13.6GB
47.2
Table 7: Efficiency comparison on Gaussian scenes of different scales. Preprocess and query time are reported for small, medium, and large scenes.