Research map / Vision-language and multimodal / Human and embodied modeling
Multi-Attribute Interactions Matter for 3D Visual Grounding
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Proposes MA2TransVG, a multi-attribute aware Transformer for 3D visual grounding that uses causal-effect attribute supervision and attribute-level token exchanging for multimodal fusion, reaching SOTA on ScanRefer, Nr3D and Sr3D.
Problem
Existing two-stage 3D visual grounding methods embed only the most salient object attributes, leading to biased unimodal representations and misaligned cross-modal fusion, especially when multiple similar objects exist.
Approach
An attribute causal analysis module quantifies each attribute attention's causal effect on the final prediction via counterfactual attentions and supervises it with a cross-entropy attribute loss; an exchanging-based fusion module replaces low-attribute-attention tokens with the mean of the other modality's tokens before fusion.
Key results
- Achieves 57.9% Acc@0.25 and 45.7% Acc@0.5 overall on ScanRefer, outperforming second-best 3DRefTR-SP (55.5/43.5).
- Reaches 65.2% overall on Nr3D and 73.9% on Sr3D, improving over ViL3DRef (64.4 / 72.8).
- Ablation on Nr3D shows attribute interactions add about 15 points (46.1 to 60.2 when adding AT/AV), and token exchanging adds 2.7 points over the causal loss alone.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add Attribute-Text attention (ablation) | MA2TransVG baseline · Nr3D | accuracy: 55.2 | Helped |
| add Attribute-Vision attention (ablation) | MA2TransVG baseline · Nr3D | accuracy: 51.9 | Helped |
| add both Attribute-Text and Attribute-Vision attention (ablation) | MA2TransVG baseline · Nr3D | accuracy: 55.2% and 51.9% -> 59.7% | Helped |
| add causal effect analysis loss (ablation) | MA2TransVG with both attribute attentions · Nr3D | accuracy: +2.7% | Helped |
| add exchanging-based multimodal token exchange (ablation) | MA2TransVG with causal effect analysis · Nr3D | accuracy: 62.4% -> 65.2% | Helped |
| use uniform instead of random counterfactual attention (ablation) | MA2TransVG · Nr3D | accuracy: 65.2 -> 64.8 | Hurt |
| use reversed instead of random counterfactual attention (ablation) | MA2TransVG · Nr3D | accuracy: 65.2 -> 64.5 | Hurt |
| use shuffled instead of random counterfactual attention (ablation) | MA2TransVG · Nr3D | accuracy: 65.2 -> 65.0 | Hurt |
| set token exchange proportion θ to 0% instead of 10% (ablation) | MA2TransVG · Nr3D | accuracy: 65.2 -> 64.4 | Hurt |
| set token exchange proportion θ to 5% instead of 10% (ablation) | MA2TransVG · Nr3D | accuracy: 65.2 -> 64.7 | Hurt |
| set token exchange proportion θ to 15% instead of 10% (ablation) | MA2TransVG · Nr3D | accuracy: 65.2 -> 63.9 | Hurt |
| set token exchange proportion θ to 20% instead of 10% (ablation) | MA2TransVG · Nr3D | accuracy: 65.2 -> 61.4 | Hurt |
Surprising findings
- Random attention as the counterfactual gave the best accuracy among tested counterfactual strategies, which the authors attribute to more diverse attribute-interaction combinations.
Limitations
- Accuracy remains lower on view-dependent descriptions than on view-independent or easy ones, especially in Sr3D.
- Authors note attribute embedding (absolute position encoding, multi-view aggregation) could be improved.
- Relies on pre-detected proposals from GroupFree, so grounding quality is bounded by the detector.
Experimental setup
Datasets: ScanRefer Nr3D Sr3D ScanNet
Benchmarks: ScanRefer Nr3D Sr3D
Metrics: Acc@0.25 Acc@0.5 accuracy
Baselines: ScanRefer ReferIt3D TGNN InstanceRefer 3DVG 3D-SPS ViL3DRef EDA 3DRefTR M3DRef-CLIP MVT UniT3D
Builds on: ViL3DRef GroupFree BERT PointNet++ EDA
Closest papers by meaning
- From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes (NeurIPS 2025 · similarity 0.87)
- Multi-Object 3D Grounding with Dynamic Modules and Language-Informed Spatial Attention (NeurIPS 2024 · similarity 0.85)
- Grounded 3D-Aware Spatial Vision-Language Modeling (CVPR 2026 · similarity 0.84)
- AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and Spatial Relation-based Referring (AAAI 2025 · similarity 0.83)
- 3EED: Ground Everything Everywhere in 3D (NeurIPS 2025 · similarity 0.83)
- CoT3DRef: Chain-of-Thoughts Data-Efficient 3D Visual Grounding (ICLR 2024 · similarity 0.83)
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