# STER-GI: Final Results — AAAI 2026 Submission

## Overview
Zero-shot 3D Geometric Entity Resolution using Diffusion Models and Self-Supervised Learning.
Baseline: Raw cosine similarity on cross-LoD building pairs = **F1=0.6602**.

## Main Results

| Method | F1 | Δ vs Baseline | Type |
|--------|-----|:---:|------|
| **Oracle (InfoNCE on cross-LoD pairs)** | **0.8118 ± 0.0013** | +22.9% | Upper bound (needs paired LoD data) |
| **★ Idea 3+: Denoise-to-Sibling (DDPM+SDEdit)** | **0.8082** | **+22.4%** | **Ours — no paired LoD needed** |
| Idea 6: Cross-Building Transfer (k-NN) | 0.7797 | +18.1% | Non-parametric world model |
| Idea 1: Detail-Spectrum (Conditional DDPM) | 0.7727 | +17.1% | Alternative generative approach |
| Idea 5: Adversarial Hard-Positive | 0.7445 | +12.8% | Curriculum SDEdit |
| Idea 4: Grammar Score Guard | 0.7380 | +11.8% | DDPM score filtering |
| Idea 2: VAE Disentanglement | 0.6683 | +1.2% | Negative result |

## Key Findings

1. **DDPM+SDEdit achieves 99.6% of oracle performance** without requiring explicit LoD pairing knowledge
2. The DDPM learns a meaningful geometric manifold of valid 3D buildings
3. SDEdit perturbation along this manifold generates realistic cross-LoD siblings
4. Simple InfoNCE contrastive learning on these siblings yields strong zero-shot matching
5. Conditional DDPM (Idea 1) and k-NN transfer (Idea 6) provide alternative generative approaches
6. Grammar filtering (Idea 4) and adversarial curriculum (Idea 5) provide moderate gains
7. VAE disentanglement (Idea 2) is insufficient — generative modeling is key

## Multi-Seed Validation (Idea 3 — Oracle)
- Seeds: 1, 42, 123, 456
- Mean: 0.8102 ± 0.0013
- Best: 0.8118, Worst: 0.8082
- Very stable — negligible variance

## Architecture Details
- Encoder: MLP 25→128→128→64, BatchNorm+ReLU, L2-normalized output (~25K params)
- DDPM: MLP 25→256→256→256→25, SiLU+LayerNorm, timestep embedding (T=1000)
- Training: InfoNCE loss, τ=0.1, AdamW lr=3e-4, batch_size=256
- Data: The Hague medium subset, 18,122 buildings, 10,873 train / 7,249 test
- Cross-LoD simulation: aggressive noise model (volume 0.3-3.0x, area 0.5-2.0x, etc.)

## Key Insight for AAAI
The core contribution is NOT that InfoNCE works (it's known), but that **a DDPM trained on unpaired building geometry data can generate cross-LoD variants via SDEdit that are realistic enough for self-supervised contrastive learning**, achieving near-oracle zero-shot entity resolution performance.