BridgeAfford: Bridging Query-Target Gap for Generalizable 3D Affordance Grounding

Yijie Su; Yakun Zhu; Yi Bin; Pengpeng Zeng; Liang Chen; Guoqing Wang; Jingkuan Song; Heng Tao Shen

cs.CVOct 15, 2026

BridgeAfford: Bridging Query-Target Gap for Generalizable 3D Affordance Grounding

Authors: Yijie Su, Yakun Zhu, Yi Bin, Pengpeng Zeng, Liang Chen, Guoqing Wang, Jingkuan Song, Heng Tao Shen

Organizations: Tongji University · University of Electronic Science and Technology of China

Abstract

Language-guided 3D affordance grounding aims to localize functional regions on 3D objects according to interaction intents. However, existing methods exhibit poor generalization to unseen objects. One challenge is the query-target gap: a semantic discrepancy between query features encoding interaction intents and target-region features, leading to inaccurate grounding. End-to-end training favors query representations tailored to seen objects, limiting their compatibility with shifted target-region features on unseen objects. To address this issue, we propose BridgeAfford, a framework that reduces the query-target semantic gap from two complementary perspectives. First, Interaction Region Alignment (IRA) promotes cross-object alignment among geometrically similar interaction regions during training. This enables structures on unseen objects that resemble previously observed ones to produce representations similar to those learned during training, facilitating target-region recognition. Second, Visual Evidence Aggregation (VEA) leverages a 2D affordance model to refine the attention distribution and guide the query toward the correct region, yielding more target-aware queries. Extensive experiments on the LASO and PIAD datasets demonstrate that BridgeAfford outperforms existing methods, particularly on unseen objects, highlighting its robust generalization ability.

Figures & tables

Figure 1: Motivation for BridgeAfford. (a) Accurate prediction relies on high semantic similarity between the query and the target-region representation. However, shifted local representations on unseen objects may prevent the model from extracting the target region, resulting in query features with low similarity to it. (b) Unseen objects may contain interaction regions that are geometrically similar to those of seen objects. Aligning these regions with geometrically similar regions on training objects promotes consistent representations, helping the model recognize interaction regions and incorporate their semantic information into the query. (c) Objects may contain novel interaction structures that cannot be reliably aligned with those observed during training based on geometric similarity. We therefore use a more generalizable 2D affordance model to directly guide feature extraction, facilitating the generation of target-aware queries.

Figure 2: Overview of BridgeAfford. (a) BridgeAfford follows the LASO pipeline and improves generalization from two sides: IRA aligns geometrically similar interaction regions across objects, while VEA lifts multi-view 2D affordance predictions into 3D as an attention bias. (b) During initialization, new region features are directly inserted when empty slots are available; otherwise, the two most similar entries are merged to release a slot for the new entry. (c) Visual evidence is incorporated as an attention bias to guide query-to-point attention toward interaction regions.

		Seen				Unseen
Method	Venue	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$
ReferTrans ( Li and Sigal, 2021 )	NeurIPS 2021	13.7	79.8	0.497	0.124	10.2	69.1	0.432	0.145
ReLA ( Liu et al., 2023 )	CVPR 2023	15.2	78.9	0.532	0.118	10.7	69.7	0.429	0.144
3D-SPS ( Luo et al., 2022 )	CVPR 2022	11.4	76.2	0.433	0.138	7.9	68.8	0.402	0.158
IAGNet ( Yang et al., 2023 )	ICCV 2023	17.8	82.3	0.561	0.109	12.9	77.8	0.443	0.129
PointRefer ( Li et al., 2024 )	CVPR 2024	20.8	87.3	0.629	0.093	14.6	80.2	0.507	0.119

Table 1: Results on the LASO dataset. Higher is better for aIoU, AUC, and SIM, while lower is better for MAE. The best and second-best results are highlighted in bold and underlined, respectively.

Intention-Driven 3D Affordance Grounding
		Seen				Unseen
Method	Venue	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$
PMF ( Zhuang et al., 2021 )	ICCV 2021	10.13	75.05	0.425	0.141	4.67	60.25	0.330	0.211
ILN ( Chen et al., 2022 )	SIGGRAPH 2022	11.25	75.84	0.427	0.137	4.71	59.69	0.325	0.207
FRCNN ( Xu et al., 2023 )	Remote Sensing 2023	11.97	76.05	0.429	0.136	4.71	61.92	0.332	0.195
PFusion ( Xu et al., 2018 )	CVPR 2018	12.31	77.50	0.432	0.135	5.33	61.87	0.330	0.193

Table 2: Results on the PIAD dataset. The best and second-best results among all methods are highlighted in bold and underlined, respectively.

Method	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$
PointRefer ( Li et al., 2024 )	17.7	83.2	0.567	0.110
GEAL ( Lu et al., 2025 )	18.5	83.8	0.582	0.096
3D-AffLLM ( Chu et al., 2025 )	17.6	82.4	0.570	–
ViSPLA ( Basak and Yin, 2025 )	19.7	84.5	0.610	–
Ours	23.6	87.0	0.633	0.080

Table 3: Cross-dataset generalization.

Figure 3: Data efficiency of our method with limited training data.

	Seen				Unseen
Variant	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$
Backbone	22.0	84.6	0.610	0.094	19.3	80.7	0.568	0.107
Backbone + IRA	23.4	85.7	0.635	0.090	20.7	82.5	0.585	0.100
Backbone + VEA	22.9	86.0	0.638	0.089	20.4	81.1	0.592	0.102
Backbone + IRA + VEA	23.7	86.8	0.646	0.086	21.8	83.0	0.597	0.099

Table 4: Main module ablation on the LASO dataset.

IRA fine-grained ablation					VEA fine-grained ablation
Variant	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$	Variant	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$
Label-based alignment	20.1	80.5	0.563	0.102	2D affordance model only	15.2	77.1	0.500	0.192
Random initialization	19.8	81.4	0.588	0.102	w/o normal-based coloring	19.1	81.0	0.565	0.108
Full IRA	21.8	83.0	0.597	0.099	w/o depth confidence	21.4	81.5	0.592	0.106
					Full VEA	21.8	83.0	0.597	0.099

Table 5: Fine-grained ablation on the LASO unseen split.

Figure 4: Qualitative comparisons on the LASO dataset (unseen).

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Hyperparameter	Value	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$
Memory size $K$	4	20.7	82.0	0.583	0.104
Memory size $K$	8	21.5	82.6	0.588	0.102
Memory size $K$	16	22.0	83.9	0.598	0.096
Memory size $K$	32	21.5	83.1	0.596	0.101
Warm-up epochs	0	20.8	82.7	0.582	0.102
Warm-up epochs	1	21.0	83.4	0.594	0.099

Appendix

Table 6: Hyperparameter selection of IRA-related components on the LASO unseen split.

Hyperparameter	Value	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$
Attention bias $\lambda_{v}$	0.1	20.7	81.5	0.584	0.109
Attention bias $\lambda_{v}$	0.5	21.6	82.8	0.594	0.100
Attention bias $\lambda_{v}$	1.0	22.0	83.9	0.598	0.096
Attention bias $\lambda_{v}$	2.0	21.3	82.1	0.589	0.103
Number of views	2	21.2	82.9	0.589	0.100
Number of views	4	21.7	83.4	0.596	0.098

Appendix

Table 7: Hyperparameter selection of VEA-related components on the LASO unseen split.

Method	Target $\uparrow$	Background $\downarrow$	$\Delta$ $\uparrow$
Backbone	$-0.009$	$-0.202$	$0.193$
Backbone + IRA	$0.045$	$-0.161$	$0.206$
Backbone + VEA	$0.021$	$\mathbf{-0.204}$	$0.225$
Backbone + IRA + VEA	0.062	$-0.183$	0.245

Appendix

Table 8: Query–region cosine similarity on the evaluation set.

\Delta

denotes target similarity minus background similarity.

Figure 5: t-SNE visualization of geometric region features after DAI initialization.

Figure 6: Visualization of renderings generated by Normal-Based Gaussian Renderer.

Method	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$
3D Backbone (Projected)	21.7	91.9	0.645	0.042
2D Affordance Model	25.2	92.2	0.704	0.040

Appendix

Table 9: Comparison of the generalization capabilities of the 2D affordance model and the 3D backbone on the LASO unseen split.

Guidance Strategy	Guidance Form	aIoU $\uparrow$	AUC $\uparrow$	SIM $\uparrow$	MAE $\downarrow$
GLANCE-style	Sparse keypoint guidance	21.2	81.9	0.585	0.103
CAMVA (Ours)	Dense point-wise evidence	21.8	83.0	0.597	0.099

Appendix

Table 10: Comparison of different visual guidance strategies on the LASO unseen split. All variants use the same backbone, 2D affordance model, multi-view predictions, and training settings.

Figure 7: Attention visualization with and without visual guidance.

Class	aIoU	AUC	SIM	MAE	Class	aIoU	AUC	SIM	MAE
bag-contain	29.40	98.21	0.796	0.092	bag-grasp	20.86	83.71	0.507	0.106
bag-lift	35.39	89.36	0.538	0.026	bag-open	13.90	83.21	0.523	0.077
bed-lay	25.38	86.16	0.625	0.103	bed-sit	13.59	85.17	0.521	0.095
bed-support	13.69	67.55	0.495	0.127	bottle-contain	25.42	87.56	0.570	0.122
bottle-grasp	26.25	81.23	0.572	0.140	bottle-open	37.19	95.83	0.517	0.047
bottle-pour	33.12	91.94	0.535	0.060	bottle-wrap_grasp	7.09	60.32	0.703	0.144

Appendix

Table 11: Per-class results on the LASO unseen split.

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Part-Aware Open-Vocabulary 3D Affordance Grounding via Prototypical Semantic and Geometric Alignment

Dongqiang Gou, Xuming He

ShanghaiTech University

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Open paper AskPDF arXiv

Grounding natural language questions to functionally relevant regions in 3D objects -- termed language-driven 3D affordance grounding -- is essential for embodied intelligence and human-AI interaction. Existing methods, while progressing from label-based to language-driven approaches, still face challenges in open-vocabulary generalization, fine-grained geometric alignment, and part-level semantic consistency. To address these issues, we propose a novel two-stage cross-modal framework that enhances both semantic and geometric representations for open-vocabulary 3D affordance grounding. In the first stage, large language models generate part-aware instructions to recover missing semantics, enabling the model to link semantically similar affordances. In the second stage, we introduce two key components: Affordance Prototype Aggregation (APA), which captures cross-object geometric consistency for each affordance, and Intra-Object Relational Modeling (IORM), which refines geometric differentiation within objects to support precise semantic alignment. We validate the effectiveness of our method through extensive experiments on a newly introduced benchmark, as well as two existing benchmarks, demonstrating superior performance in comparison with existing methods.

Abstract

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