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CoT3DRef: Chain-of-Thoughts Data-Efficient 3D Visual Grounding
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Proposes CoT3DRef, which reformulates 3D visual grounding as a sequence-to-sequence chain-of-anchors-then-target prediction, using automatically generated pseudo-labels (no human anchor annotations) to plug into existing 3D grounding models.
Problem
3D visual grounding models localize the referred object directly with a referring head, which fails in complex scenarios, offers no interpretability, and needs a lot of training data.
Approach
Predict an ordered chain of anchor objects mentioned in the utterance (ordering via a Pathway head trained on GPT-3.5 labels) and then localize the target with a causal CoT transformer decoder that attends to previously localized anchors; anchor labels come from rule-based parsing and localization instead of human annotation.
Key results
- On Nr3D, CoT3DRef on MVT with pseudo labels reaches 60.4% overall versus 55.1% baseline; with human anchor labels reaches 64.4%.
- On Sr3D with only 10% of training data, the MVT+CoT variant matches the MVT/SAT baselines trained on 100% of the data, per the paper's claim.
- On ScanRefer with MVT at 100% data, CoT3DRef reaches 64.2% vs 57.4% for MVT; with SAT it reaches 60.3% vs 53.8%.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add CoT3DRef to LAR without GT anchors | LAR · Nr3D | accuracy: 48.9 -> 52.5 | Helped |
| Add CoT3DRef to MVT | MVT · Nr3D+Sr3D | accuracy: 58.5 ± 0.2 -> 62.5±0.4 | Helped |
| Add CoT3DRef to MVT | MVT · ScanRefer, 10% data | accuracy: 36.4 -> 48.6 | Helped |
| Add CoT3DRef to MVT | MVT · ScanRefer, 100% data | accuracy: 57.4 -> 64.2 | Helped |
| Add CoT instead of parallel anchor and target localization (ablation) | MVT · Nr3D, 10% data | accuracy: 31.7 -> 37.5 | Helped |
| Add CoT instead of parallel anchor and target localization (ablation) | MVT · Sr3D, 10% data | accuracy: 55.3 -> 65.2 | Helped |
| Add distractor loss to CoT (ablation) | MVT+CoT · Nr3D, 100% data | accuracy: 60.0 -> 60.4 | Helped |
| Add distractor loss to CoT (ablation) | MVT+CoT · Sr3D, 100% data | accuracy: 72.7 -> 73.2 | Helped |
| Increase CoT decoder depth from 1 to 2 blocks (ablation) | MVT+CoT3DRef · Nr3D | accuracy: 60.4 -> 60.4 | No clear effect |
| Increase CoT decoder depth from 1 to 4 blocks (ablation) | MVT+CoT3DRef · Nr3D | accuracy: 60.4 -> 60.1 | Hurt |
| Replace pseudo anchor labels with human labels (ablation) | MVT+CoT3DRef · Nr3D | target referring accuracy: 60.4 -> 64.4 | Helped |
| Zero the anchor localization loss (ablation) | MVT+CoT3DRef · Nr3D | target referring accuracy: 60.4 -> 55.0 | Hurt |
Surprising findings
- Zeroing the anchor localization loss (random anchor predictions) left target accuracy near baseline levels, suggesting the target gain is not solely due to accurate anchor localization.
- Integration with ViL3DRel gave only marginal gains on full data, attributed by the authors to an upper bound from annotation errors.
Limitations
- Pseudo-label anchor localization gives a noticeable accuracy gap versus ground truth, limiting gains on Nr3D.
- Pathway module does not handle multiple valid orderings; anchor localization is tailored to ScanNet labels.
- Gains on ViL3DRel are marginal.
Experimental setup
Datasets: Nr3D Sr3D ScanRefer ScanNet
Benchmarks: Nr3D Sr3D ScanRefer
Metrics: grounding accuracy standard deviation across runs anchor localization accuracy edit distance for pathway ordering
Baselines: MVT SAT LAR ViL3DRel PhraseRefer ScanEnts 3D-SPS LanguageRefer TransRefer3D 3DVG-Trans
Builds on: MVT SAT LAR ViL3DRel chain-of-thought prompting
Closest papers by meaning
- From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes (NeurIPS 2025 · similarity 0.91)
- Grounded 3D-Aware Spatial Vision-Language Modeling (CVPR 2026 · similarity 0.91)
- Chain of Semantics Programming in 3D Gaussian Splatting Representation for 3D Vision Grounding (CVPR 2025 · similarity 0.90)
- AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and Spatial Relation-based Referring (AAAI 2025 · similarity 0.90)
- Viewpoint-Aware Visual Grounding in 3D Scenes (CVPR 2024 · similarity 0.89)
- SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding (CVPR 2025 · similarity 0.89)
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