# LiteSAM Feature Matcher Verification Report

**Research date**: 2025-03-14  
**Scope**: Satellite-aerial image matching, boyagesmile/LiteSAM, Remote Sensing MDPI Oct 2025

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## 1. GitHub Repository Verification

| Aspect | Finding | Source | Confidence |
|--------|---------|--------|------------|
| **Repo exists** | Yes, https://github.com/boyagesmile/LiteSAM | GitHub API | High |
| **Stars** | 5 | GitHub API (stargazers_count: 5) | High |
| **Forks** | 0 | GitHub API (forks_count: 0) | High |
| **Open issues** | 0 | GitHub API (open_issues_count: 0) | High |
| **Last commit** | 2025-10-01 (Update README.md) | GitHub API commits | High |
| **First commit** | 2025-09-24 (Initial commit) | GitHub API commits | High |
| **Total commits** | 4 | GitHub API | High |
| **Actively maintained** | Low — no commits since Oct 2025, no releases, no license | GitHub API | High |
| **Releases** | None | GitHub API | High |
| **License** | None declared | GitHub API | High |

**Conclusion**: Repo is real but minimal. Not actively maintained (no commits in ~5 months). Very low community engagement.

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## 2. Pretrained Weights Availability

| Aspect | Finding | Source | Confidence |
|--------|---------|--------|------------|
| **Weights location** | Google Drive | README.md | High |
| **Download link** | https://drive.google.com/file/d/1fheBUqQWi5f55xNDchumAx2-SmGdT-mX/view?usp=drive_link | README.md | High |
| **File name** | mloftr.ckpt | Google Drive page title | High |
| **Downloadable** | Yes — link resolves to Google Drive file page (requires sign-in for direct download) | Web fetch | Medium |
| **Alternative hosts** | None — no HuggingFace, no Zenodo | Search | High |

**Conclusion**: Weights are available via Google Drive as stated in the draft. Single point of failure; no mirror or checksum documented.

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## 3. Claimed Results Verification

### 3.1 Clarification: 77.3% Hit Rate

| Claim in draft | Actual source | Dataset | Confidence |
|----------------|---------------|---------|-------------|
| "77.3% hit rate in Hard mode" | Paper Table 4 | **Self-made dataset** (Harbin/Qiqihar, 100–500m altitude) | High |
| UAV-VisLoc Hard hit rate | Paper Table 3 | **61.65%** (not 77.3%) | High |

**Important**: The 77.3% figure applies to the **self-made dataset**, not UAV-VisLoc. UAV-VisLoc Hard mode hit rate is 61.65%.

### 3.2 UAV-VisLoc Results (Paper Table 3)

| Method | RMSE@30 | Easy HR | Moderate HR | Hard HR | Inference (RTX 3090) |
|--------|---------|---------|-------------|---------|---------------------|
| LiteSAM | 17.86 m | 66.66% | 65.37% | 61.65% | 83.79 ms |
| EfficientLoFTR | 17.87 m | 65.78% | 63.62% | 57.65% | 112.60 ms |
| SP+LG | 17.81 m | 60.34% | 59.57% | 54.32% | 44.15 ms |

### 3.3 Self-made Dataset Results (Paper Table 4)

| Method | RMSE@30 | Easy HR | Moderate HR | Hard HR | Inference |
|--------|---------|---------|-------------|---------|-----------|
| LiteSAM | 6.12 m | 92.09% | 87.88% | **77.30%** | 85.31 ms |
| EfficientLoFTR | 7.28 m | 90.03% | 79.79% | 61.84% | 120.72 ms |
| SP+LG | 6.76 m | 78.85% | 70.03% | 58.31% | 49.49 ms |

### 3.4 Independent Reproduction

| Aspect | Finding | Source | Confidence |
|--------|---------|--------|------------|
| **Third-party reproduction** | None found | Web search "LiteSAM reproduced results", "LiteSAM satellite matching community" | Medium |
| **Note** | Search results conflate with Lite-SAM (ECCV 2024 segmentation model) | Web search | High |
| **Author reproduction** | Paper states SP+LG and EfficientLoFTR (opt.) on HPatches were "reproduced by the authors under unified experimental environment" | Paper Section 4.3 | High |
| **LiteSAM reproduction** | No explicit statement of third-party reproduction | — | — |

**Conclusion**: Paper numbers are internally consistent. No evidence of independent reproduction. Name collision with Lite-SAM (segmentation) complicates discovery.

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## 4. GPU and RTX 2060 Performance

| Aspect | Finding | Source | Confidence |
|--------|---------|--------|------------|
| **Paper benchmark GPU** | NVIDIA RTX 3090 | Paper Section 4.2 | High |
| **Resolution** | 1184×1184 (UAV-VisLoc, self-made) | Paper | High |
| **LiteSAM inference (RTX 3090)** | 83.79 ms (full), 60.97 ms (opt.) | Paper Table 3 | High |
| **RTX 2060 estimate** | ~140–210 ms (PyTorch, no ONNX) | Project fact cards (Fact #14) | Medium |
| **Rationale** | RTX 2060 Turing ≈ 40–60% of RTX 3090 throughput | General GPU knowledge | Medium |
| **Measured RTX 2060** | Not found — no published benchmarks | Search | High |

**Conclusion**: RTX 3090 numbers are from the paper. RTX 2060 figures are estimates only; no measured data found.

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## 5. ONNX / TensorRT Export

| Aspect | Finding | Source | Confidence |
|--------|---------|--------|------------|
| **LiteSAM ONNX** | None — no export script or docs | README, paper, GitHub | High |
| **LiteSAM TensorRT** | None | README, paper, GitHub | High |
| **EfficientLoFTR ONNX** | loftr2onnx exists for **original LoFTR** (not EfficientLoFTR) | Web search, loftr2onnx repo | High |
| **EfficientLoFTR TensorRT** | LoFTR_TRT targets original LoFTR | Kolkir/LoFTR_TRT | High |
| **EfficientLoFTR HuggingFace** | Yes — Transformers integration, no ONNX export in repo | HuggingFace | High |
| **Convertibility** | Theoretically possible — PyTorch model; no documented path | Architecture analysis | Low |

**Conclusion**: No ONNX or TensorRT path for LiteSAM. EfficientLoFTR has HuggingFace support but no official ONNX; LoFTR family ONNX/TensorRT work targets original LoFTR. LiteSAM conversion would require custom effort.

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## 6. Parameters and VRAM

| Aspect | Finding | Source | Confidence |
|--------|---------|--------|------------|
| **Parameters** | 6.31M (claimed) | Paper Table 4, Section 3.1 | High |
| **EfficientLoFTR params** | 15.05M (2.4× more) | Paper | High |
| **MobileOne-S3 backbone** | 0.81M params (after removing classification head) | Paper Section 3.1 | High |
| **FLOPs (self-made, 1184×1184)** | 588.51G (LiteSAM) vs 1036.61G (EfficientLoFTR) | Paper Table 4 | High |
| **VRAM (measured)** | Not reported in paper | Paper | High |
| **VRAM (estimated)** | ~300–500 MB (model + feature maps at 1184×1184) | Project fact cards (Fact #15) | Medium |
| **Model file size** | mloftr.ckpt — size not documented | Google Drive | Low |

**Conclusion**: 6.31M parameters confirmed. VRAM is estimated, not measured.

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## 7. Architecture and EfficientLoFTR Relationship

| Aspect | Finding | Source | Confidence |
|--------|---------|--------|------------|
| **Built on EfficientLoFTR** | Yes — acknowledged in README | README Acknowledgement | High |
| **Key differences** | MobileOne-S3 backbone (replaces RepVGG), TAIFormer (replaces EfficientLoFTR attention), MinGRU (replaces heatmap refinement) | Paper Section 3 | High |
| **EfficientLoFTR maturity** | 964 stars, 96 forks, 55 open issues, CVPR 2024, HuggingFace, Apache 2.0 | GitHub API | High |
| **EfficientLoFTR ONNX** | loftr2onnx exists for original LoFTR; EfficientLoFTR-specific ONNX not clearly documented | Web search | Medium |
| **EfficientLoFTR TensorRT** | LoFTR_TRT (original LoFTR); no direct EfficientLoFTR TensorRT | Web search | High |

**Conclusion**: LiteSAM is built on EfficientLoFTR. EfficientLoFTR is mature (CVPR 2024, HuggingFace). LiteSAM uses a different backbone and modules; ONNX/TensorRT work for LoFTR does not directly apply.

---

## 8. Remote Sensing (MDPI) Journal Reputation

| Aspect | Finding | Source | Confidence |
|--------|---------|--------|------------|
| **Venue** | Remote Sensing, MDPI | Paper | High |
| **Impact Factor** | 4.1 (2024), 5-year 4.8 | MDPI announcements | High |
| **CiteScore** | 8.6 (June 2025) | MDPI | High |
| **Ranking** | Q1 in General Earth and Planetary Sciences, Geosciences, Remote Sensing | MDPI | High |
| **Indexing** | Scopus, Web of Science (SCIE) | MDPI | High |
| **Publication date** | 30 September 2025 | Paper | High |
| **Reputation** | Established Q1 journal in remote sensing | Multiple sources | High |

**Conclusion**: Remote Sensing (MDPI) is a reputable Q1 venue for this topic.

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## 9. Search Query Results Summary

| Query | Result |
|-------|--------|
| "LiteSAM satellite matching" | Results dominated by Lite-SAM (ECCV 2024 segmentation). boyagesmile/LiteSAM satellite matcher rarely appears. |
| "LiteSAM feature matching UAV" | Same name collision. No specific UAV feature-matching discussions for boyagesmile/LiteSAM. |
| "LiteSAM vs EfficientLoFTR" | Lite-SAM (segmentation) vs EfficientLoFTR (matching) — different tasks. No direct comparison of boyagesmile/LiteSAM vs EfficientLoFTR. |
| GitHub issues | 0 open issues. No reported bugs. |
| Community discussions | None found. No Reddit, Twitter/X, or forum threads specific to boyagesmile/LiteSAM. |

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## 10. What Could NOT Be Verified

| Item | Reason |
|------|--------|
| Independent reproduction of paper results | No third-party reports found |
| Actual VRAM usage | Not in paper or repo |
| RTX 2060 inference time | No benchmarks; estimate only |
| Google Drive weight checksum | Not provided |
| ONNX conversion feasibility | No attempt documented; would need implementation |
| Real-world performance on Ukraine/conflict-zone terrain | Paper uses Chinese datasets (Harbin, Qiqihar, UAV-VisLoc) |
| Long-term weight availability | Google Drive link could change or be removed |

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## 11. Summary Table

| Question | Answer | Confidence |
|----------|--------|------------|
| 1. Repo real and maintained? | Real, minimal maintenance (5 stars, 0 forks, 4 commits, last Oct 2025) | High |
| 2. Weights available? | Yes, Google Drive; single host, no checksum | High |
| 3. Results reproduced? | No evidence of third-party reproduction | Medium |
| 4. GPU used? | RTX 3090 | High |
| 5. RTX 2060 performance? | ~140–210 ms (estimate only) | Medium |
| 6. ONNX export? | No | High |
| 7. Parameters? | 6.31M (confirmed) | High |
| 8. VRAM? | ~300–500 MB (estimated) | Medium |
| 9. Built on EfficientLoFTR? | Yes | High |
| 10. EfficientLoFTR mature? | Yes (CVPR 2024, 964 stars, HuggingFace) | High |
| 11. Remote Sensing reputable? | Yes (Q1, IF 4.1) | High |

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## 12. Draft Correction

**Draft says**: "77.3% hit rate in Hard mode on satellite-aerial benchmarks"

**Clarification**: 77.3% is on the **self-made dataset** (Harbin/Qiqihar, 100–500 m). On **UAV-VisLoc**, Hard mode hit rate is **61.65%**. Both are satellite-aerial benchmarks, but the numbers differ by dataset.
