Research map / Detection and segmentation / Open-set and few-shot detection
RG-SAN: Rule-Guided Spatial Awareness Network for End-to-End 3D Referring Expression Segmentation
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Proposes RG-SAN, an end-to-end 3D referring expression segmentation model that localizes all text-mentioned entities in the point cloud and supervises them only via the target's position, with dependency-tree rules selecting the target token.
Problem
3D referring expression segmentation (3D-RES) on point clouds suffers from over- or mis-segmentation because models under-use spatial relationships among entities mentioned in the expression, and only target instances carry positional supervision.
Approach
A Text-driven Localization Module assigns each word token a 3D position via text-point similarity and iteratively refines it with offsets and positional-aware attention; a Rule-guided Weak Supervision strategy uses dependency-tree rules to pick the target noun so only the target's position label supervises the spatial localization of all nouns.
Key results
- On ScanRefer overall, RG-SAN reaches 44.6 mIoU, 5.1 points above 3D-STMN (39.5), and 61.7 Acc@0.25.
- On the Multiple subset, mIoU improves from 31.1 (3D-STMN) to 37.4 (+6.3).
- Inference time is 295ms versus 283ms for 3D-STMN, reported as competitive.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add RG-SAN spatial reasoning to 3D-STMN | 3D-RES · ScanRefer overall | mIoU: 39.5 -> 44.6; increase of 5.1 points | Helped |
| add RG-SAN spatial reasoning to 3D-STMN | 3D-RES · ScanRefer overall | Acc@0.25: 54.6 -> 61.7; increase of 7.1 points | Helped |
| add RG-SAN spatial reasoning | 3D-RES · ScanRefer Multiple | mIoU: 31.1 -> 37.4; 6.3-point increase | Helped |
| use text-driven initialization for both embeddings and positions (ablation) | RG-SAN TLM · ScanRefer overall | mIoU: 40.3 -> 42.3 | Helped |
| add positional supervision (ablation) | RG-SAN · ScanRefer overall | mIoU: 38.9 -> 42.3 | Helped |
| use table-based relative positional encoding instead of no positional encoding (ablation) | RG-SAN · ScanRefer overall | mIoU: 41.1 -> 42.3 | Helped |
| use Fourier absolute positional encoding instead of no positional encoding (ablation) | RG-SAN · ScanRefer overall | mIoU: 41.1 -> 40.7 | Hurt |
| use RTS dependency rules instead of attention-based Top1 selection (ablation) | RG-SAN RWS · ScanRefer overall | mIoU: 42.3 -> 44.6 | Helped |
| use the dependency-tree root token instead of attention-based Top1 selection (ablation) | RG-SAN RWS · ScanRefer overall | mIoU: 42.3 -> 42.5 | Helped |
| add iterative position refinement (ablation) | RG-SAN TLM · ScanRefer overall | mIoU: 43.7 -> 44.6 | Helped |
| increase TLM rounds from 6 to 9 (ablation) | RG-SAN TLM · ScanRefer overall | mIoU: 44.6 -> 44.2 | Hurt |
| replace MPNet with CLIP (ablation) | RG-SAN · ScanRefer overall | mIoU: 44.6 -> 42.5 | Hurt |
Surprising findings
- Absolute positional encoding alone performs worse than using no positional encoding (40.7 vs 41.1 overall mIoU for Fourier APE vs w/o PE).
- Llama 2 70B localizes the target word in about 80% of ScanRefer cases matching the rule-based RWS, per the appendix.
Limitations
- Single-point localization struggles with plural nouns.
- Insufficient robustness to damaged or incomplete point clouds.
- Relies on manually designed dependency-tree rules.
Experimental setup
Datasets: ScanRefer ScanNet ReferIt3D (Nr3D, Sr3D)
Benchmarks: ScanRefer ReferIt3D Nr3D ReferIt3D Sr3D
Metrics: mIoU Acc@0.25 Acc@0.5
Baselines: 3D-STMN 3DRefTR EDA TGNN InstanceRefer 3D-SPS 3DVG-Transformer X-RefSeg3D 3DRESTR UniSeg3D SegPoint Reason3D
Builds on: 3D-STMN MAFT SPFormer MPNet Table-based RPE
Closest papers by meaning
- ReferSplat: Referring Segmentation in 3D Gaussian Splatting (ICML 2025 · similarity 0.90)
- 3D-STMN: Dependency-Driven Superpoint-Text Matching Network for End-to-End 3D Referring Expression Segmentation (AAAI 2024 · similarity 0.89)
- X-RefSeg3D: Enhancing Referring 3D Instance Segmentation via Structured Cross-Modal Graph Neural Networks (AAAI 2024 · similarity 0.87)
- Boosting Weakly Supervised Referring Image Segmentation via Progressive Comprehension (NeurIPS 2024 · similarity 0.87)
- LESS: Label-Efficient and Single-Stage Referring 3D Segmentation (NeurIPS 2024 · similarity 0.86)
- Hybrid Global-Local Representation with Augmented Spatial Guidance for Zero-Shot Referring Image Segmentation (CVPR 2025 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.