## 📌 Repository <!-- Required: paste the GitHub repository URL of the matcher --> Repository URL: https://github.com/HITCSC/CLIDD Ckpt URL: https://github.com/HITCSC/CLIDD/releases/tag/v1.0 <!-- Required: link to the paper --> Paper (arXiv / conference): https://arxiv.org/abs/2601.09230 ## 🔧 Matcher Info <!-- Required --> Type: - [x] **Sparse** (extractor + matcher, e.g., SuperPoint+LightGlue) - [ ] **Dense / Standalone** (end-to-end, e.g., LoFTR, RoMa) <!-- Optional but helpful --> Efficiency: - [x] High (real-time) - [ ] Medium - [ ] Low (slow, research-grade) ## ✅ Checklist <!-- Mark with `[x]` what applies --> - [x] The repository has an open-source license (MIT, Apache, BSD, etc.) - [x] The model has pretrained weights available - [x] The code is compatible with PyTorch - [x] I understand the matcher will be added as a git submodule under `imcui/third_party/` ## 📝 Notes <!-- Any special dependencies, known issues, or additional context? -->
📌 Repository
Repository URL: https://github.com/HITCSC/CLIDD
Ckpt URL: https://github.com/HITCSC/CLIDD/releases/tag/v1.0
Paper (arXiv / conference): https://arxiv.org/abs/2601.09230
🔧 Matcher Info
Type:
Efficiency:
✅ Checklist
imcui/third_party/📝 Notes