# Component Fit Matrix — C3: Cross-domain registration (Matchers)

> Mode A Phase 2 — engine Step 7.5 (Component Applicability Gate, structured per-component candidate-selection table). Status vocabulary in [`00_summary.md`](00_summary.md). Detailed fact cards backing every status verdict live in [`../02_fact_cards/C3_matchers.md`](../02_fact_cards/C3_matchers.md).
>
> Index: [`00_summary.md`](00_summary.md). Sibling components: [C1 VIO](C1_vio.md), [C2 VPR](C2_vpr.md), [C4 Pose](C4_pose_estimation.md), [C5–C10 pending](C5-C10_pending.md). Cross-component gates: [`99_cross_component_gates.md`](99_cross_component_gates.md).

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## C3 — Cross-domain registration (matcher) [mandatory pre-screen CLOSED at documentary level 2026-05-08 at 5 of N candidates per user decision; mandatory-simple-baseline role STRUCTURALLY COMPLETE per engine Component Option Breadth rule; modern-competitive-lead axis MATERIALLY EXPANDED with XFeat lightweight-CNN structurally-different design point; final ranking deferred to Jetson MVE phase]

**Pinned mode** (per-image-pair sparse-matcher contract for the project's C3 row):

- inputs: `{1× ADTi 20MP nav frame stream → grayscale-converted + bilinearly downscaled-to-largest-edge 1024 + canonical satellite tile per top-K retrieval result from C2 (NetVLAD/MixVPR/SALAD/SelaVPR/EigenPlaces)}` on `Jetson Orin Nano Super (8 GB shared, JetPack 6, ROS 2 Humble; PyTorch fp16 baseline; final inference runtime selection deferred to C7 + D-C3-2)`; per-frame compute = K=10 image pairs (UAV-frame, satellite-tile) per Fact #25 + AC-3.3 re-localization
- outputs: `{up to 1024 2D-2D correspondences with confidence scores per (UAV-frame, satellite-tile) image pair, feeding the downstream C4 PnP+RANSAC pose estimator with cosine confidence threshold filter at 0.95 × per-pair-max-score}`

**Locked-in research-time defaults** (carried forward from C1 + C2 — Fact #41):
- D-C1-1 = (c) **keep both license tracks open** through Plan; final license decision deferred to post-Jetson-MVE.
- D-C1-2 = (b) **defer Jetson Orin Nano Super hardware MVE to a dedicated bring-up phase** between research and Plan; research closes with documentary ranking + per-candidate `Verify` gates.

| # | Candidate | License | Per-mode verification | Status | Lead reason / disqualifier | Sub-matrix cite |
|---|---|---|---|---|---|---|
| 1 | **SP+LightGlue** (SuperPoint MagicLeap-pretrained + LightGlue `features='superpoint'` @ 1024×1024 grayscale → up to 1024 2D-2D correspondences + confidence scores) | **Apache-2.0** on `cvg/LightGlue` matcher itself (BSD/permissive track on matcher) + **Magic Leap "ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY"** on canonical SuperPoint pretrained weights AND `lightglue/superpoint.py` inference file (**HARD DISQUALIFIER for canonical SP+LightGlue mode in project's dual-use deployment context**) | ✅ Fact #47 + Source #69 + #70 + #71 + #72 + #73 | **Documentary lead with Apache-2.0 matcher + Magic-Leap-restrictive-extractor-weights HARD DISQUALIFIER on canonical SP+LightGlue + DISK+LightGlue Apache-2.0 mitigation RECOMMENDED + adaptive-depth/adaptive-width pruning advantage + actively-maintained Jetson ONNX/TensorRT/FP16/FP8 export pathway (FP8 Ampere emulation verification gate) + TIGHT latency-budget interaction at K=10 pairs/frame caveat + aerial-domain-training caveat (D-C2-1 reuse)** | Canonical ICCV 2023 implementation (`cvg/LightGlue`, Lindenberger+Sarlin+Pollefeys ETH Zurich + Microsoft); five canonical extractor-matcher sibling modes (SP+LightGlue, **DISK+LightGlue [Apache-2.0 throughout, paper Table 6 +7.99 absolute AUC@5° on IMC 2020 stereo over SP+LightGlue]**, ALIKED+LightGlue [BSD-3-Clause + Apache-2.0], SIFT+LightGlue [classical patent-free], DoGHardNet+LightGlue); paper Table 3 documentary evidence Aachen Day-Night with NetVLAD top-50 + SP+LightGlue + PnP+RANSAC pipeline = direct documentary equivalence to project's intended pipeline shape (C2 → C3 → C4); canonical RTX-3080 throughput 150 FPS @ 1024 keypoints with adaptivity (= 6.7 ms per pair) / 50 FPS @ 4096 keypoints; Jetson Orin Nano Super extrapolation ~30-60 ms per pair @ 1024 keypoints at fp16+TensorRT standard / ~15-30 ms with adaptive depth (paper §5.4 1.86× speedup on easy pairs); model footprint ~27 MB at fp16 (smallest of any C-row component evaluated so far); zero C3 cache footprint (C3 operates on UAV-frame + retrieved-tile pair on-the-fly with no pre-cached match-time state). **Three NEW caveats**: (i) HARD LICENSE DISQUALIFIER on canonical SuperPoint pretrained weights (Source #72 Magic Leap noncommercial-research-only Software License Agreement) — blocks commercial AND dual-use deployment per project's question_decomposition.md hard disqualifier; mitigation via D-C3-1 NEW SuperPoint-replacement-strategy choice with **DISK+LightGlue (Apache-2.0 throughout) RECOMMENDED + paper Appendix A Table 6 documentary technical superiority**; (ii) TIGHT latency-budget interaction at K=10 top-K retrieval pairs per frame — 10 pairs × 30-60 ms = 300-600 ms standard / 150-300 ms adaptive against AC-4.1 400 ms budget before C1+C2+C5+C8 costs added → D-C3-3 NEW K-pairs-per-frame Plan-phase choice; (iii) Jetson Orin Nano Super FP8 emulation on Ampere uncertain (Source #73 documents FP8 ModelOpt workflow on Hopper/Ada/Blackwell — Jetson Orin Nano Super is Ampere) → D-C3-2 NEW LightGlue-inference-runtime Plan-phase choice. **POSITIVE structural advantages** vs other C-row components: **structural geometric-verification advantage over C2 single-stage retrieval** (catches mid-flight-written misaligned tiles via per-correspondence confidence + RANSAC inlier selection — addresses AC-NEW-7 cache-poisoning safety budget where C2 candidates have no structural advantage); **adaptive-depth + adaptive-width pruning** (paper §3.3 ~33% average inference-time reduction at <1% accuracy loss, up to 1.86× speedup on easy pairs); **Apache-2.0 placement on cvg/LightGlue itself** (independent of extractor's weight license) places matcher on BSD/permissive track | `../02_fact_cards/C3_matchers.md` → "SP+LightGlue — per-numbered binding" |
| 2 | **DISK+LightGlue** (DISK extractor with `weights='depth'` canonical default at 1024-largest-edge RGB input + up to 1024 keypoints with 128-D L2-normalised descriptors + LightGlue `features='disk'` @ 1024×1024 → up to 1024 2D-2D correspondences with confidence scores; canonical 4-layer U-Net with deformable bottleneck; two pretrained-weights sibling modes: DISK-depth canonical default / DISK-epipolar supplementary-material alternate) — **D-C3-1 RECOMMENDED-PRIMARY-MITIGATION** for SuperPoint license-disqualifier on canonical SP+LightGlue mode | **Apache-2.0 throughout** (canonical cvlab-epfl/disk extractor confirmed via GitHub API metadata `license.spdx_id: "Apache-2.0"` + cvg/LightGlue matcher Apache-2.0 + kornia integration layer Apache-2.0 = FULLY CLEAN Apache-2.0 license track on every layer of the stack) | ✅ Fact #49 + Source #76 + #77 (cross-cite Source #70 + #71 + #73 + #75) | **Documentary lead with FULLY-CLEAN-APACHE-2.0 license track THROUGHOUT + PAPER-TABLE-6-+7.99-ABSOLUTE-AUC@5°-DOCUMENTARY-TECHNICAL-SUPERIORITY-OVER-CANONICAL-SP+LIGHTGLUE + LIGHTGLUE-ONNX-TENSORRT-EXPORT-PATHWAY-PRESENT (FROM JUN 2023) + 98.97-GFLOPS-HIGHEST-RAW-COMPUTE-COST CAVEAT (MEDIUM-RISK D-C3-2) + RL-POLICY-GRADIENT-TRAINING-RETRAIN-COST CAVEAT + aerial-domain-training caveat (D-C2-1 reuse) + D-C3-5 NEW DISK-pretrained-weights-choice** | **CLEANEST license-compliant LightGlue-extractor-sibling** in the project's evaluated C3 candidate space + **strongest documentary technical-superiority signal vs canonical SP+LightGlue**. **Three converging POSITIVE structural advantages**: (i) **FULLY CLEAN APACHE-2.0 license track THROUGHOUT** — Apache-2.0 on canonical cvlab-epfl/disk extractor (Source #76 GitHub API metadata `license.spdx_id: "Apache-2.0"`) + Apache-2.0 on cvg/LightGlue matcher (Source #70) + Apache-2.0 on kornia integration layer = single Apache-2.0 license throughout, no Magic Leap noncommercial-research disqualifier (vs SP+LightGlue) and no BSD-3-Clause + Apache-2.0 mixed track (vs ALIKED+LightGlue); eligible on every D-C1-1 license-posture path with the simplest license-compliance story; (ii) **PAPER APPENDIX A TABLE 6 +7.99 ABSOLUTE AUC@5° on IMC 2020 STEREO over canonical SP+LightGlue** (DISK+LightGlue 67.02 vs SP+LightGlue 59.03 = +7.99 absolute + AUC@10°=83.45 vs SP+LightGlue 77.96 = +5.49 absolute = strongest documentary technical-superiority signal in the project's evaluated C3 candidate space, demonstrably technically-superior to canonical SP+LightGlue on phototourism stereo per Source #71 paper documentation); (iii) **LIGHTGLUE-ONNX TENSORRT EXPORT PATHWAY PRESENT** (Source #73 30 Jun 2023 changelog "DISK feature extraction support added" + parallel CLI commands to SP+LightGlue + 19 Jul 2023 TensorRT support + 04 Oct 2023 MultiHead-Attention fusion + FlashAttention-2 fused ONNX up to 80% faster + 02 Nov 2023 TopK trick ~30% speedup) — DISK+LightGlue is **second-cleanest Jetson-deployment-ready LightGlue-extractor-sibling** after SP+LightGlue, **before** ALIKED+LightGlue (export-absent in LightGlue-ONNX as of January 2026). **Three NEGATIVE structural findings vs ALIKED+LightGlue**: (iv) **HIGHER raw GFLOPs at competitive accuracy** — DISK 98.97 GFLOPs vs ALIKED-N(16) 4.05 GFLOPs = **24.4× higher GFLOPs** (LightGlue-ONNX TensorRT pathway partially mitigates ~3-5× speedup over PyTorch fp16, but does not eliminate the raw-GFLOPs gap); on Jetson Orin Nano Super extrapolation DISK+LightGlue with TensorRT ≈ 50-100 ms per pair vs ALIKED+LightGlue PyTorch-fp16-only ≈ 70-140 ms per pair; PyTorch-fp16-only fallback for DISK+LightGlue (~200-400 ms per pair) FAILS AC-4.1 budget at K=10 pairs/frame; (v) **Lower MHA@3 on HPatches homography accuracy** — DISK 70.56 vs ALIKED-N(16) 77.22 = -6.66 absolute (DISK's evenly-distributed dense keypoints give weaker per-pixel geometric verification accuracy); (vi) **Worse Aachen Day-Night relocalization at strictest tier** — DISK at (0.25m,2°)=70.4 vs ALIKED-N(32)=77.6 = -7.2 absolute (DISK is stronger on phototourism stereo while ALIKED is stronger on visual-localization). **One ADDITIONAL CAVEAT**: (vii) **HIGH RL-policy-gradient training cost** — canonical training takes ~2 weeks on 32 GB V100 OR ~2 weeks at smaller batch on 12 GB low-memory variant; vs ALIKED's ~24 hours on RTX 3090 = DISK is **materially less retrain-friendly than ALIKED** at the GPU-memory + wall-clock level for D-C2-1 = (a) project-domain retrain decision; **HOWEVER, well-documented retrain workflow** via canonical `colmap/colmap2dataset.py` workflow (much better-documented than SP-reproduction which requires Magic-Leap's Homographic Adaptation training pipeline + LICENSE clearance). **D-C3-5 NEW Plan-phase decision**: DISK-pretrained-weights-choice (`save-depth.pth` canonical default with full documentary IMW2020 + IMC 2020 numbers / `save-epipolar.pth` supplementary-material alternate variant with -0.5 to -1 absolute AUC trade-off / project-domain retrain on aerial nadir corpus); for the project's pinned UAV-vs-satellite-tile registration use case **`save-depth.pth` is the recommended canonical default**. **D-C3-2 PREFERRED runtime path for DISK+LightGlue**: ONNX Runtime + TensorRT EP via Source #73 (DOMINANT path; vs ALIKED+LightGlue's PyTorch-fp16-only DOMINANT path) | `../02_fact_cards/C3_matchers.md` → "DISK+LightGlue — per-numbered binding" |
| 3 | **ALIKED+LightGlue** (ALIKED-N(16) canonical extractor at 1024-largest-edge RGB input, 1024 max keypoints, 128-D L2-normalised descriptors + LightGlue `features='aliked'` @ 1024×1024 → up to 1024 2D-2D correspondences with confidence scores; four canonical extractor sibling modes: ALIKED-T(16) 64-D / N(16) 128-D / N(16rot) 128-D rotation-augmented / N(32) 128-D higher-SDDH-sample-count) | **BSD-3-Clause** (canonical Shiaoming/ALIKED weights + cvg/LightGlue `lightglue/aliked.py` port inheritance) + **Apache-2.0** (cvg/LightGlue matcher) — clean BSD/permissive throughout | ✅ Fact #48 + Source #74 + #75 (cross-cite Source #70 + #71 + #73) | **Documentary lead with BSD-3-Clause-canonical license track + ALIKED-export-absence-in-LightGlue-ONNX HARSHER-D-C3-2-gate caveat + drastic-GFLOPs-reduction advantage + best-Aachen-Day-Night-relocalization-on-canonical-paper advantage + aerial-domain-training caveat (D-C2-1 reuse) + D-C3-4 NEW ALIKED-sibling-mode-choice** | **Second-cleanest license-compliant alternative** to canonical SP+LightGlue's Magic-Leap-restrictive disqualifier (D-C3-1 SECONDARY-MITIGATION role after DISK+LightGlue's RECOMMENDED-PRIMARY); BSD-3-Clause canonical extractor (Source #74) + Apache-2.0 matcher (Source #70) = clean BSD/permissive license track throughout, eligible on every D-C1-1 license-posture choice. **Three POSITIVE structural advantages**: (i) **DRASTIC GFLOPs REDUCTION at competitive accuracy** — paper Table IV ALIKED-N(16) 4.05 GFLOPs vs SuperPoint 26.11 GFLOPs (-6.4×) vs DISK 98.97 GFLOPs (-24.4×), **PPC=12.91 = 24.8× higher than DISK's 0.52** — most-Jetson-friendly modern competitive sparse extractor on a GFLOPs-per-accuracy basis; (ii) **BEST Aachen Day-Night relocalization** at strictest tier — paper Table VII ALIKED-N(32) at (0.25m,2°)/(0.5m,5°)/(5m,10°)=77.6/88.8/100.0 with 2048 keypoints = **+8.2 absolute over SuperPoint=69.4 + +7.2 absolute over DISK=70.4** at strictest tier on the project-relevant visual-localization task; (iii) **MHA@3=77.22% on HPatches homography accuracy** vs SuperPoint MHA@3=70.19% = +7.03 absolute (paper Table IV) — deformable descriptor extraction provides better geometric verification accuracy. **Two NEGATIVE structural findings**: (iv) **HARSHER D-C3-2 Jetson export-pathway gate** — Source #73 (`fabio-sim/LightGlue-ONNX`) does NOT ship documented ALIKED end-to-end ONNX/TensorRT pipeline as of January 2026 (changelog + citations + CLI examples support SuperPoint + DISK only); ALIKED's `torchvision.ops.deform_conv2d` is a known-difficult ONNX export op; project's Jetson runtime path for ALIKED+LightGlue is restricted to **(a) PyTorch-fp16 only (DOMINANT path), (b) custom ONNX export with deform_conv plugin (significant engineering effort), (c) Torch-TensorRT partial graph compilation with deform_conv falling back to PyTorch-eager** vs DISK+LightGlue's well-documented LightGlue-ONNX TensorRT pathway; (v) DISK+LightGlue has +7.99 absolute AUC@5° on IMC 2020 stereo per Source #71 paper Table 6 — DISK+LightGlue is stronger on phototourism stereo while ALIKED is stronger on visual-relocalization (Aachen). **D-C3-4 NEW Plan-phase decision**: ALIKED-sibling-mode choice (T(16) Jetson-friendliest 64-D / N(16) canonical baseline 128-D / N(16rot) rotation-augmented 128-D / N(32) Aachen-best 128-D); for project's UAV multi-heading 1 km AGL flights + Jetson PyTorch-fp16-only deployment, **ALIKED-N(16rot) is the strongest sibling-mode candidate** (rotation augmentation aligns with multi-heading aerial flights; 4.05 GFLOPs leaves K=10 pairs/frame headroom; same 128-D as canonical). MODERATE retrain-friendliness (paper §V sparse NRE loss reduces GPU memory ~3.5× vs DISK's RL training; canonical training 100K steps over MegaDepth + R2D2 homographic at 800×800 batch 2 with gradient accumulation × 6 — feasible on a single RTX 3090 in ~24 hours) | `../02_fact_cards/C3_matchers.md` → "ALIKED+LightGlue — per-numbered binding" |
| 4 | **XFeat / XFeat\*** (lightweight CNN extractor + matcher; canonical `verlab/accelerated_features` CVPR 2024; THREE primary inference modes: XFeat sparse with `top_k=4096` keypoints + 64-D float descriptors + Mutual Nearest Neighbor MNN matching / XFeat\* semi-dense with up to 10k features + 2-scale processing 0.65× + 1.3× resize + MNN + lightweight MLP-based offset refinement / XFeat+LighterGlue paired-matcher with VerLab-trained smaller LightGlue ~3× faster than canonical LightGlue) | **Apache-2.0 throughout** (canonical verlab/accelerated_features extractor + matcher confirmed via GitHub API metadata `license.spdx_id: "Apache-2.0"` + cvg/LightGlue cross-cite for LighterGlue companion mode + cvg/glue-factory training framework Apache-2.0 = CLEAN Apache-2.0 license track on every layer of the stack, **TIED with DISK+LightGlue as cleanest license-compliant C3 candidate**) | ✅ Fact #51 + Source #80 + #81 (cross-cite Source #70 + #71 + #73) | **Documentary lead with CLEAN-APACHE-2.0-LICENSE-THROUGHOUT (TIED-CLEANEST with DISK+LightGlue) + STRONGEST DOCUMENTED EMBEDDED-DEPLOYMENT SIGNAL AMONG ALL C3 CANDIDATES EVALUATED + STRONGEST RETRAIN-FRIENDLINESS SIGNAL AMONG ALL C3 CANDIDATES EVALUATED + NO-PRODUCTIZED-ONNX/TENSORRT-EXPORT-PATHWAY-CAVEAT (D-C3-2 HARSHER than DISK BUT TECHNICALLY SIMPLER than ALIKED) + MegaDepth-1500-sparse-mode-modestly-below-LightGlue-siblings CAVEAT (XFeat+LighterGlue narrows gap) + aerial-domain-training caveat (D-C2-1 reuse but XFeat is CHEAPEST retrain) + D-C3-6 NEW XFeat-mode-choice** | **MODERN-COMPETITIVE-LEAD reference for the C3 row's lightweight-CNN axis** (structurally-different design point from all LightGlue-extractor-siblings: lightweight CNN with decoupled keypoint detection + lightweight MLP-based match refinement vs LightGlue's transformer-based attention matcher). **Three converging POSITIVE structural advantages**: (i) **CLEAN APACHE-2.0 LICENSE TRACK THROUGHOUT** — Apache-2.0 on canonical verlab/accelerated_features extractor + Apache-2.0 on cvg/LightGlue matcher (LighterGlue companion) + Apache-2.0 on cvg/glue-factory training framework = single Apache-2.0 license throughout, no copyleft + no Magic Leap restrictive disqualifier; **TIED-CLEANEST license-compliant LightGlue-extractor-sibling-or-modern-competitive-lead** in C3 candidate space alongside DISK+LightGlue's RECOMMENDED-PRIMARY-MITIGATION role; eligible on every D-C1-1 license-posture path with the cleanest license-compliance story; (ii) **STRONGEST DOCUMENTED EMBEDDED-DEPLOYMENT SIGNAL AMONG ALL C3 CANDIDATES EVALUATED** — Source #81 paper Appendix C explicit Orange Pi Zero 3 ($28 ARM Cortex-A53 device) at 480×360 input documents XFeat=**1.8 FPS** vs SuperPoint=0.16 FPS (11.25× faster) vs ALIKE=0.58 FPS (3.1× faster); paper explicitly states "XFeat is the ONLY learned method capable of running over 1 FPS on highly-constrained embedded device" without neural-network-inference optimization; Source #80 README explicit "Simple architecture components which facilitates deployment on embedded devices (jetson, raspberry pi, custom AI chips, etc..)"; **Jetson Orin Nano Super extrapolation** XFeat sparse PyTorch-fp16 ~10-30 ms per pair (vs ALIKED's ~70-140 ms / DISK's ~200-400 ms / SP+LightGlue's ~30-60 ms) — strongest extrapolated latency advantage among all C3 candidates evaluated; (iii) **STRONGEST RETRAIN-FRIENDLINESS SIGNAL AMONG ALL C3 CANDIDATES EVALUATED** — Source #81 paper §3.3 + Appendix B explicit "trained on a single NVIDIA RTX 4090 GPU, consuming 6.5 GB of VRAM in total" + 36 hours total convergence; paper §3.3 explicit "low memory usage of our method enables training on entry-level hardware, facilitating the fine-tuning or full training of our network for specific tasks and scene types"; materially cheaper than DISK+LightGlue (~2 weeks 32 GB V100) + comparable to ALIKED+LightGlue (~24 hours RTX 3090) + infinitely better than SuperGlue+SuperPoint (training-code-not-released). **One NEGATIVE structural finding**: (iv) **NO PRODUCTIZED ONNX/TensorRT EXPORT PATHWAY** in canonical repo (Source #80 README Contributing section explicit community-contribution ask "Currently, it would be nice to have an export script to efficient deployment engines such as TensorRT and ONNX"); **D-C3-2 gate is HARSHER than DISK+LightGlue's well-documented LightGlue-ONNX TensorRT pathway (Source #73) but TECHNICALLY SIMPLER than ALIKED+LightGlue's `torchvision.ops.deform_conv2d` ONNX-export blocker** because XFeat is CNN-only with no deformable convolutions or unusual ops (Conv + ReLU + BatchNorm only); project would need to invest custom-ONNX-export engineering effort but the architecture is straightforward at moderate cost (~1-2 weeks vs ALIKED's ~4-6 weeks deform_conv2d-plugin engineering effort). **Two ADDITIONAL CAVEATS**: (v) **MegaDepth-1500 sparse-mode AUC@5°=42.6 is materially below DISK 53.8 + DISK\* 55.2 + ALIKE-Tiny 49.4** at strictest tier per Source #81 paper Table 1 — XFeat sparse positioned as "competitive at much higher speed" rather than "best-accuracy"; XFeat+LighterGlue narrows the gap to -2.5 to -2.8 absolute below SP+LightGlue per Source #80 README cross-cite (Fast/Accurate configs); (vi) **AERIAL-DOMAIN-TRAINING CAVEAT** shared with all C3 candidates evaluated (canonical training on MegaDepth phototourism + COCO_20k synthetic-warp pairs at 6:4 ratio — NOT aerial nadir; **D-C2-1 reuse with XFeat-cheapest-retrain advantage** at 36 hours single RTX 4090). **Documentary headline performance** (per Source #81 paper Table 1 MegaDepth-1500 i5-1135G7 CPU VGA): XFeat sparse AUC@5/10/20 = **42.6/56.4/67.7 at 27.1 FPS = 9× faster than SuperPoint at HIGHER AUC + 5× faster than ALIKE-Tiny**; XFeat\* semi-dense AUC@5/10/20 = **50.2/65.4/77.1 at 19.2 FPS = comparable to DISK\* at 16× speedup with 1885 inliers per pair vs LightGlue 475 inliers (4× more inliers per pair via Source #81 Appendix F Table 6)**; ScanNet-1500 indoor (paper Table 2): **XFeat outperforms ALL baselines including SuperPoint+DISK+ALIKE on indoor cross-domain transfer** despite all methods being MegaDepth-trained (paper Appendix E hybrid-training generalization advantage); HPatches Homography (paper Table 3) MHA@3 Illumination/Viewpoint = **95.0/68.6 = best illumination@3 in paper Table 3 across all evaluated methods**. **D-C3-6 NEW Plan-phase decision**: XFeat-mode-choice between (a) XFeat sparse with MNN matching for SIMPLEST deployment (no separate matcher network required, fewest moving parts; D-C3-2 fully sidesteps cvg/LightGlue dependency for the standalone-extractor mode), (b) XFeat\* semi-dense with MNN+offset-refinement for HIGHEST inlier count per pair (1885 vs LightGlue 475 = 4× more inliers per pair, valuable for downstream C4 PnP+RANSAC stability), (c) XFeat+LighterGlue paired-matcher for MODERN learned-matcher accuracy with VerLab-trained LighterGlue ~3× faster than canonical LightGlue. **D-C3-1 ALTERNATE-MODERN-COMPETITIVE-LEAD role** (alongside DISK+LightGlue's RECOMMENDED-PRIMARY-MITIGATION) — XFeat is the alternate cleanest license-compliant + structurally-different design point + materially-cheapest-retrain-cost C3 candidate | `../02_fact_cards/C3_matchers.md` → "XFeat — per-numbered binding" |
| 5 | **SuperGlue+SuperPoint** (canonical SuperPoint MagicLeap-pretrained extractor at 1024×1024 grayscale + canonical SuperGlue matcher with `superglue='outdoor'` MegaDepth-trained checkpoint, `nms_radius=3`, `match_threshold=0.2` @ up to 1024 keypoints with 256-D descriptors → up to 1024 2D-2D correspondences with confidence scores; two pretrained-weights sibling modes documented: superglue_indoor.pth ScanNet-trained / superglue_outdoor.pth MegaDepth-trained) | **Magic Leap "ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY"** Software License Agreement on BOTH canonical SuperPoint pretrained weights (Source #72) AND canonical SuperGlue matcher implementation+weights (Source #78 LICENSE byte-for-byte identical wording) — **HARD DISQUALIFIER** for project's dual-use deployment context | ✅ Fact #50 + Source #78 + #79 (cross-cite Source #71 + #72 + #73) | **Mandatory simple-baseline (sparse-matcher) — DOCUMENTARY PASS at the role with THREE CONVERGING HARD-DISQUALIFIERS for project deployment (Magic-Leap-restrictive license byte-for-byte identical to Source #72 + TRAINING-CODE-NOT-RELEASED + 4-10×-SLOWER-THAN-LIGHTGLUE-PER-SOURCE-#71-PAPER-§5) + aerial-domain-training caveat (D-C2-1 reuse but BLOCKED by training-code-not-released)** | **MANDATORY-SIMPLE-BASELINE role per engine Component Option Breadth rule** — SuperGlue+SuperPoint is the **canonical sparse-matcher mandatory-simple-baseline reference** for the C3 row, structurally analogous to NetVLAD's mandatory-simple-baseline role in the C2 row. The role's purpose is to establish the long-established graph-neural-network sparse-matcher reference floor against which modern leads (LightGlue, XFeat) must measurably exceed at deployment-ready license + Jetson-friendly runtime + retrain-capable training. **NOT a Selected candidate path** for project deployment due to **THREE CONVERGING HARD-DISQUALIFIERS**: (i) **HARD LICENSE DISQUALIFIER** — Source #78 LICENSE wording is **byte-for-byte identical** to Source #72 SuperPoint LICENSE = Magic Leap noncommercial-research-only SLA, blocks dual-use deployment; (ii) **TRAINING CODE NOT RELEASED** — Source #78 README explicit "We do not intend to release the SuperGlue training code" — D-C2-1 retrain decision is **STRUCTURALLY BLOCKED** for SuperGlue+SuperPoint pinned mode (no project-side mitigation pathway exists, unlike SP+LightGlue where LightGlue training code IS released and SP-reproduction with permissive license is a documented mitigation); (iii) **4-10× SLOWER THAN LIGHTGLUE** at competitive but slightly lower accuracy per Source #71 LightGlue paper §5 + Table 2 documentary evidence — Jetson Orin Nano Super extrapolation puts SP+SuperGlue at ~600-1200 ms per pair fp16 = **catastrophic AC-4.1 FAIL** even at K=1 pair/frame (vs 400 ms budget); at K=10 pairs/frame = 6-12 seconds = catastrophic fail. **ADDITIONAL NEGATIVE structural findings**: (iv) **NO ALTERNATIVE EXTRACTOR PAIRING** — paired exclusively with canonical Magic Leap SuperPoint extractor (no SIFT or homography variants released per Source #78 README); (v) **NO STRUCTURAL ADVANTAGES OVER LIGHTGLUE** — no FlashAttention support, no adaptive-depth/adaptive-width pruning (LightGlue paper §3.3 ~33% inference-time reduction); (vi) **NO PRODUCTIZED Jetson ONNX/TensorRT export pathway** (Source #73 LightGlue-ONNX repo supports SP+LightGlue + DISK+LightGlue extractors only, NO SuperGlue end-to-end ONNX/TensorRT pipeline; SuperGlue ONNX export is community-maintained third-party only). **Documentary results in canonical README** (Source #78 evaluation tables): ScanNet test (1500 indoor pairs) AUC@5/10/20 = 16.12/33.76/51.79; YFCC test (4000 outdoor pairs) AUC@5/10/20 = 39.02/59.51/75.72 — establishes the documented reference floor against which DISK+LightGlue / SP+LightGlue / ALIKED+LightGlue measurably exceed per Source #71 paper Table 2 cross-cite (LightGlue is 4-10× faster + 1-3 absolute AUC higher). **POSITIVE for the role**: SuperGlue+SuperPoint **IS** the canonical sparse-matcher mandatory-simple-baseline reference that the engine's Component Option Breadth rule requires to be cataloged — confirms the engine rule's purpose: cataloging the simple-baseline FORCES modern leads (DISK+LightGlue + ALIKED+LightGlue + SP+LightGlue) to demonstrate measurable advantage on documented-evidence axes (4-10× speedup, training-code-released for retrain capability, and either Apache-2.0 throughout (DISK) or BSD-3-Clause + Apache-2.0 mixed (ALIKED) license-track placement). **NO new Plan-phase decision raised by SuperGlue+SuperPoint closure** — the mandatory-simple-baseline role is structural; the project's deployment will not select SuperGlue+SuperPoint regardless of D-C1-1 license-posture choice because TRAINING-CODE-NOT-RELEASED + 4-10×-SLOWER are independent disqualifiers from the license disqualifier. **NO REUSE of D-C3-2 Jetson runtime path choice** — Source #73 LightGlue-ONNX does NOT support SuperGlue end-to-end ONNX/TensorRT export | `../02_fact_cards/C3_matchers.md` → "SuperGlue+SuperPoint — per-numbered binding" |
| 6 | **MASt3R** (dense matcher) | (project repo — verify) | (pruned by Fact #26 dense-matcher Jetson latency disqualifier — reference-only) | **Pruned outright — disqualified by AC-4.1** | Dense-matcher latency on Jetson Orin Nano Super exceeds AC-4.1 budget per Fact #26 NGPS template; reference-only candidate (not in active pre-screen) | (no sub-matrix; pruned on Fact #26) |
| 7 | **RoMa** (dense matcher) | (verify) | (pruned by Fact #26 — reference-only) | **Pruned outright — disqualified by AC-4.1** | Same as MASt3R — dense-matcher latency disqualifier on Jetson | (no sub-matrix; pruned on Fact #26) |
| 8 | **DKM** (dense matcher) | (verify) | (pruned by Fact #26 — reference-only) | **Pruned outright — disqualified by AC-4.1** | Same as MASt3R / RoMa — dense-matcher latency disqualifier on Jetson | (no sub-matrix; pruned on Fact #26) |
| 9 | **LoFTR** (dense matcher reference baseline) | Apache-2.0 | (mentioned in Source #71 paper Table 2 as dense-matcher comparator — reference-only baseline; competitive with sparse SP+LightGlue at fraction of inference time advantage to sparse) | **Pruned outright — disqualified by AC-4.1 dense-matcher latency** | Paper Table 2 MegaDepth-1500 AUC@5°/10°/20°=66.4/78.6/86.5 — competitive accuracy with SP+LightGlue 66.7/79.3/87.9, but dense-matcher inference cost on Jetson exceeds AC-4.1 budget. Reference-only baseline | (no sub-matrix; pruned on Fact #26 + paper Table 2) |
| 10 | **GIM+DKM, GIM+LightGlue** | (verify) | (reference-only per Fact #26) | **Reference-only** | GIM cross-domain training paradigm; subordinate to canonical LightGlue + extractor candidates; reference-only at this stage | (reference-only) |

### C3 — Plan-phase deliverables raised by SP+LightGlue + ALIKED+LightGlue + DISK+LightGlue + SuperGlue+SuperPoint + XFeat closures (5 of N mandatory pre-screen; mandatory-simple-baseline role STRUCTURALLY COMPLETE per engine Component Option Breadth rule; modern-competitive-lead axis MATERIALLY EXPANDED with XFeat lightweight-CNN structurally-different design point; XFeat closure raises **D-C3-6 NEW** XFeat-mode-choice between sparse / semi-dense / +LighterGlue paired-matcher; conditional candidates may compound)

1. **D-C3-1 (NEW from SP+LightGlue closure 2026-05-08) — SuperPoint-replacement-strategy choice (DISK+LightGlue with Apache-2.0 + paper Table 6 superiority [RECOMMENDED] / ALIKED+LightGlue with BSD-3-Clause+Apache-2.0 / SuperPoint-reproduction-with-permissive-license / accept-Magic-Leap-noncommercial-with-swap-commitment / SIFT+LightGlue classical-baseline-fallback)** — mandatory Plan-phase decision; canonical SuperPoint pretrained weights LICENSE (Source #72 Magic Leap noncommercial-research-only Software License Agreement) is a **HARD DISQUALIFIER** on the canonical SP+LightGlue mode in the project's dual-use deployment context (eastern/southern Ukraine fixed-wing UAV with AC-NEW-2 spoofing-promotion path is dual-use military by every reasonable interpretation). **Recommendation: D-C3-1 = (a) DISK+LightGlue** — Apache-2.0 throughout AND paper Appendix A Table 6 documentary technical superiority over canonical SP+LightGlue (+7.99 absolute AUC@5° on IMC 2020 stereo).
2. **D-C3-2 (NEW from SP+LightGlue closure 2026-05-08) — LightGlue-inference-runtime choice (PyTorch-fp16 / Torch-TensorRT / ONNX Runtime + TensorRT EP via Source #73 / pure TensorRT via trtexec + Polygraphy via Source #73 / FP8 ModelOpt-on-Jetson if Ampere FP8 emulation works)** — Plan-phase decision; Source #73 (`fabio-sim/LightGlue-ONNX`) is the canonical reference for ONNX / TensorRT / OpenVINO / FP16 / FP8 export pathway with January 2026 active maintenance; Jetson Orin Nano Super is Ampere architecture (NOT FP8-native Hopper/Ada/Blackwell) — FP8 ModelOpt path applies only with INT8 emulation fallback (verification gate at Jetson MVE phase). Likely rolls into the C7 cross-cutting integration row.
3. **D-C3-3 (NEW from SP+LightGlue closure 2026-05-08) — K-pairs-per-frame budget choice (reduce K from 10 to 3-5 / reduce keypoints from 1024 to 512 / accept TIGHT 300-600 ms standard ÷ 150-300 ms adaptive margin and validate at Jetson MVE / parallelize matcher across multiple Jetson GPU streams / elevate ONNX Runtime + TensorRT EP + adaptive depth)** — Plan-phase decision; canonical RTX-3080 throughput 150 FPS @ 1024 keypoints with adaptivity → Jetson Orin Nano Super extrapolation ~30-60 ms per pair → at K=10 top-K retrieval pairs per UAV frame = 300-600 ms standard / 150-300 ms adaptive against AC-4.1 400 ms budget — TIGHT before C1+C2+C5+C8 costs added. **Three-way interaction with AC-4.1 latency budget + AC-3.3 re-localization recall + AC-1.1/1.2 frame-center pose accuracy**. Adaptive-depth path (paper §5.4 1.86× speedup on easy pairs) is the most-favorable structural trade-off if many of the K pairs are high-overlap UAV-vs-cached-tile pairs.
4. **D-C2-1 reuse (VPR canonical-weights vs aerial-retrain vs aerial-community-checkpoint)** — applies identically to LightGlue (canonical training on synthetic homographies of Oxford-Paris 1M distractors + MegaDepth phototourism is NOT aerial nadir; same caveat as C2 candidates); **D-C2-1 retrain decision interacts with D-C3-1 extractor choice** — DISK+LightGlue Apache-2.0 retrain on aerial nadir corpus is the cleanest license-compliant + retrain-friendly pathway. ALIKED+LightGlue is **moderately retrain-friendly** (paper §V sparse NRE loss reduces GPU memory ~3.5× vs DISK's RL training; feasible on a single RTX 3090 in ~24 hours), and ALIKED-N(16rot) sibling mode provides aerial-multi-heading-aligned rotation-augmented prior.
5. **D-C3-4 (NEW from ALIKED+LightGlue closure 2026-05-08) — ALIKED-sibling-mode choice (ALIKED-T(16) 64-D Jetson-friendliest @ 1.37 GFLOPs / 125.87 FPS RTX 2060 / ALIKED-N(16) 128-D canonical baseline @ 4.05 GFLOPs / 77.40 FPS / ALIKED-N(16rot) 128-D rotation-augmented for UAV multi-heading flights @ same arch as N(16) / ALIKED-N(32) 128-D higher-SDDH-sample-count Aachen-Day-Night-best @ 4.62 GFLOPs / 75.64 FPS)** — applies only if D-C3-1 = (b) ALIKED+LightGlue secondary mitigation is selected. Plan-phase decision; for project's UAV multi-heading 1 km AGL flights + Jetson PyTorch-fp16-only deployment (forced by ALIKED-export-absence in LightGlue-ONNX), **recommendation is ALIKED-N(16rot)** (rotation-augmentation aligns with multi-heading; 4.05 GFLOPs leaves K=10 pairs/frame headroom; same 128-D descriptor as canonical N(16)). ALIKED-T(16) is the **latency-fallback** if AC-4.1 budget pressure forces 64-D descriptor reduction; ALIKED-N(32) is the **accuracy-prioritization** choice if Aachen-Day-Night documentary lift is the primary axis. Interacts with D-C3-3 K-pairs-per-frame budget choice (T(16)'s 1.37 GFLOPs allows higher K than N(16)/N(32)'s 4.05/4.62 GFLOPs).
6. **D-C3-5 (NEW from DISK+LightGlue closure 2026-05-08) — DISK-pretrained-weights-choice (`save-depth.pth` canonical default with full documentary IMW2020 + IMC 2020 numbers — RECOMMENDED / `save-epipolar.pth` supplementary-material alternate variant with -0.5 to -1 absolute AUC trade-off vs `save-depth.pth` per canonical paper §6.2 / project-domain retrain on aerial nadir corpus via canonical `colmap/colmap2dataset.py` workflow at ~2 weeks on 32 GB V100 cost)** — applies only if D-C3-1 = (a) DISK+LightGlue RECOMMENDED-PRIMARY-MITIGATION is selected. Plan-phase decision; for project's pinned UAV-vs-satellite-tile registration use case **`save-depth.pth` is the recommended canonical default** (strongest documentary IMW2020 stereo + multiview AUC numbers + cross-paper Aachen Day-Night transitive lift via ALIKED paper Table VII). `save-epipolar.pth` is the fallback if depth-map ground-truth is unavailable for aerial-domain retrain. Interacts with D-C2-1 retrain decision (DISK retrain via `colmap/colmap2dataset.py` is well-documented but materially expensive ~2 weeks on 32 GB V100 / ~2 weeks at smaller batch on 12 GB low-memory variant — vs ALIKED's ~24 hours on RTX 3090).
7. **D-C3-6 (NEW from XFeat closure 2026-05-08) — XFeat-mode-choice (XFeat sparse with MNN matching for SIMPLEST deployment / XFeat\* semi-dense with MNN+MLP-offset-refinement for HIGHEST inlier count per pair / XFeat+LighterGlue paired-matcher for MODERN learned-matcher accuracy)** — applies only if XFeat is selected as a Documentary lead. Plan-phase decision; (a) **XFeat sparse with MNN matching** is the simplest deployment with no separate matcher network required and fewest moving parts; D-C3-2 LightGlue-inference-runtime decision is fully SIDESTEPPED (no cvg/LightGlue dependency for the standalone-extractor mode); (b) **XFeat\* semi-dense with MNN+MLP-offset-refinement** provides 1885 inliers per pair vs LightGlue 475 = 4× more inliers per pair per Source #81 Appendix F Table 6, valuable for downstream C4 PnP+RANSAC stability; (c) **XFeat+LighterGlue paired-matcher** uses VerLab-trained LighterGlue (~3× faster than canonical LightGlue per Source #80 README claim) trained via cvg/glue-factory framework — narrows the MegaDepth-1500 sparse-mode gap to -2.5 to -2.8 absolute below SP+LightGlue (Fast/Accurate configs per Source #80 README cross-cite); D-C3-2 LightGlue-inference-runtime decision REUSES (community-contribution-needed for productized LighterGlue export pathway). For project's pinned UAV-vs-satellite-tile registration use case + AC-NEW-7 cache-poisoning safety budget + AC-3.3 re-localization stability, **(b) XFeat\* semi-dense is the strongest documentary structural choice** (4× more inliers per pair via lightweight MLP refinement provides best RANSAC stability at lowest engineering complexity — no LightGlue dependency, no productized-export dependency). Interacts with D-C2-1 retrain decision (XFeat is the cheapest retrain candidate among all C3 candidates evaluated at 36 hours single RTX 4090 + 6.5 GB VRAM total per Source #81 §3.3) and D-C3-2 (only XFeat+LighterGlue mode reuses D-C3-2 cvg/LightGlue runtime path; sparse + semi-dense modes sidestep entirely).

### C3 — Per-license-track preliminary ranking (mandatory pre-screen CLOSED at documentary level 2026-05-08 at 5/N candidates per user decision; mandatory-simple-baseline role STRUCTURALLY COMPLETE + modern-competitive-lead axis MATERIALLY EXPANDED with structurally-different design point; final ranking deferred to Jetson MVE phase)

Status: **5 of N mandatory pre-screen candidates CLOSED at documentary level (user-locked closure 2026-05-08)** with per-Mode entries (SP+LightGlue full per-mode entry with DISK+LightGlue cross-cite, ALIKED+LightGlue full per-mode entry, DISK+LightGlue full per-mode entry, SuperGlue+SuperPoint mandatory-simple-baseline full per-mode entry, and XFeat modern-competitive-lead full per-mode entry with three primary inference modes). **Mandatory-simple-baseline role STRUCTURALLY COMPLETE per engine Component Option Breadth rule** — SuperGlue+SuperPoint closes the mandatory-simple-baseline reference role at 1-of-1. **Modern-competitive-lead axis MATERIALLY EXPANDED** with XFeat's structurally-different design point (lightweight CNN with decoupled keypoint detection + lightweight MLP-based match refinement vs LightGlue's transformer-based attention matcher) — the C3 row's modern-competitive-lead axis has the **most diverse design-point coverage of any component row in the project** (LightGlue-extractor-siblings: SP+LightGlue / DISK+LightGlue / ALIKED+LightGlue + lightweight-CNN: XFeat with three inference modes). **Closure rationale**: TWO TIED-CLEANEST license-compliant Documentary leads (DISK+LightGlue D-C3-1 RECOMMENDED-PRIMARY-MITIGATION + XFeat D-C3-1 ALTERNATE-MODERN-COMPETITIVE-LEAD) + ALIKED+LightGlue D-C3-1 SECONDARY-MITIGATION + SP+LightGlue documentary baseline + SuperGlue+SuperPoint mandatory-simple-baseline = sufficient design-point + license-track + retrain-friendliness coverage for the Plan-phase decision deck (D-C3-1 through D-C3-6 + interactions with D-C1-1 + D-C2-1 are well-formed). Candidates DEFERRED-but-not-pursued: DoGHardNet+LightGlue (additional `cvg/LightGlue` extractor-matcher sibling per Fact #26), SIFT+LightGlue (classical-detector pairing per Fact #26), additional sparse-matcher reference candidates per Fact #26 NGPS template — may be revisited if the Plan-phase deck surfaces an unmet axis or the Jetson MVE eliminates current Documentary leads.

**BSD/permissive track on matcher** (track lead under D-C1-1 = (b) or default (c)):
1. **DISK+LightGlue (D-C3-1 RECOMMENDED-PRIMARY-MITIGATION)** — Documentary lead with **CLEANEST license-compliant LightGlue-extractor-sibling** in the project's evaluated C3 candidate space + **strongest documentary technical-superiority signal vs canonical SP+LightGlue** (paper Appendix A Table 6 +7.99 absolute AUC@5° + +5.49 absolute AUC@10° on IMC 2020 stereo). **Three converging positive structural advantages**: (i) FULLY CLEAN APACHE-2.0 license track THROUGHOUT (Apache-2.0 on canonical cvlab-epfl/disk extractor + Apache-2.0 on cvg/LightGlue matcher + Apache-2.0 on kornia integration layer = single Apache-2.0 license throughout, simplest license-compliance story; eligible on every D-C1-1 license-posture path); (ii) PAPER TABLE 6 DOCUMENTARY TECHNICAL SUPERIORITY vs canonical SP+LightGlue on phototourism stereo; (iii) LIGHTGLUE-ONNX TENSORRT EXPORT PATHWAY PRESENT (Source #73 30 Jun 2023 changelog) — second-cleanest Jetson-deployment-ready LightGlue-extractor-sibling after SP+LightGlue, before ALIKED+LightGlue. **Three negative structural findings vs ALIKED+LightGlue**: (iv) 98.97 GFLOPs HIGHEST raw-compute-cost (24.4× higher than ALIKED-N(16); LightGlue-ONNX TensorRT pathway partially mitigates ~3-5× speedup but does not eliminate); (v) lower MHA@3 on HPatches homography (DISK 70.56 vs ALIKED-N(16) 77.22 = -6.66 absolute); (vi) worse Aachen Day-Night relocalization at strictest tier (DISK 70.4 vs ALIKED-N(32) 77.6 at 2048 keypoints / 0.25m,2°). **One additional caveat**: HIGH RL-policy-gradient training cost ~2 weeks on 32 GB V100 vs ALIKED's ~24 hours on RTX 3090. Carries D-C3-5 NEW (DISK-pretrained-weights-choice). **PREFERRED runtime path: ONNX Runtime + TensorRT EP via Source #73**.
2. **SP+LightGlue (canonical)** — Documentary lead with Apache-2.0 matcher + Magic-Leap-restrictive-extractor-weights HARD DISQUALIFIER on canonical SP+LightGlue + **DISK+LightGlue Apache-2.0 mitigation RECOMMENDED-PRIMARY** + adaptive-depth/adaptive-width pruning advantage + actively-maintained Jetson ONNX/TensorRT/FP16/FP8 export pathway (Source #73, Ampere FP8 emulation verification gate at Jetson MVE phase) + TIGHT latency-budget interaction at K=10 pairs/frame caveat + aerial-domain-training caveat (D-C2-1 reuse). **Hard-license-disqualified for project's dual-use deployment context** — lead status preserved only as documentary baseline reference; D-C3-1 RECOMMENDED-PRIMARY-MITIGATION (DISK+LightGlue) supersedes for actual deployment.
3. **ALIKED+LightGlue (D-C3-1 SECONDARY-MITIGATION)** — Documentary lead with three positive structural advantages (drastic GFLOPs reduction at competitive accuracy = 24.8× higher PPC than DISK; best Aachen Day-Night relocalization at strictest tier per paper Table VII ALIKED-N(32) +8.2 absolute over SuperPoint at 0.25m,2° / 2048 keypoints; +7.03 absolute MHA@3 on HPatches homography over SuperPoint via deformable descriptor extraction) and two negative structural findings (HARSHER D-C3-2 Jetson export-pathway gate due to LightGlue-ONNX ALIKED-absence + `torchvision.ops.deform_conv2d` ONNX-export-difficulty → PyTorch-fp16-only Jetson runtime path; DISK+LightGlue stronger on phototourism stereo). **D-C3-1 SECONDARY-MITIGATION role**: second-cleanest license-compliant alternative after DISK+LightGlue's RECOMMENDED-PRIMARY. **Best documentary visual-relocalization candidate** (Aachen Day-Night), but Jetson deployment story is materially weaker than DISK+LightGlue. Carries D-C3-4 NEW (ALIKED-sibling-mode-choice).

**Modern-competitive-lead with lightweight-CNN structurally-different design point — TIED-CLEANEST-LICENSE alongside DISK+LightGlue (D-C3-1 ALTERNATE-MODERN-COMPETITIVE-LEAD role)**:

4. **XFeat (D-C3-1 ALTERNATE-MODERN-COMPETITIVE-LEAD)** — Documentary lead with **CLEAN APACHE-2.0 LICENSE TRACK THROUGHOUT (TIED-CLEANEST with DISK+LightGlue)** + **STRONGEST DOCUMENTED EMBEDDED-DEPLOYMENT SIGNAL AMONG ALL C3 CANDIDATES EVALUATED** + **STRONGEST RETRAIN-FRIENDLINESS SIGNAL AMONG ALL C3 CANDIDATES EVALUATED**. **Three converging POSITIVE structural advantages**: (i) clean Apache-2.0 license track throughout (eligible on every D-C1-1 license-posture path with the cleanest license-compliance story TIED with DISK+LightGlue); (ii) **STRONGEST EMBEDDED-DEPLOYMENT SIGNAL** — Source #81 paper Appendix C Orange Pi Zero 3 ARM Cortex-A53 1.8 FPS at 480×360 input vs SuperPoint=0.16 FPS / ALIKE=0.58 FPS = 11.25× / 3.1× speedup; paper explicitly states "XFeat is the ONLY learned method capable of running over 1 FPS on highly-constrained embedded device" without optimization at 2024 publication time; Jetson Orin Nano Super extrapolation XFeat sparse PyTorch-fp16 ~10-30 ms per pair = strongest extrapolated latency advantage among all C3 candidates evaluated; (iii) **STRONGEST RETRAIN-FRIENDLINESS SIGNAL** — Source #81 paper §3.3 explicit "trained on a single NVIDIA RTX 4090 GPU, consuming 6.5 GB of VRAM in total" + 36 hours total convergence; materially cheaper than DISK+LightGlue (~2 weeks 32 GB V100) + comparable to ALIKED+LightGlue (~24 hours RTX 3090) + infinitely better than SuperGlue+SuperPoint (training-code-not-released). **One NEGATIVE structural finding**: (iv) **NO PRODUCTIZED ONNX/TensorRT EXPORT PATHWAY** in canonical repo (Source #80 README Contributing section explicit community-contribution ask) — D-C3-2 gate HARSHER than DISK+LightGlue's productized LightGlue-ONNX TensorRT pathway BUT TECHNICALLY SIMPLER than ALIKED+LightGlue's deform_conv2d ONNX-export blocker because XFeat is CNN-only with no deformable convolutions or unusual ops (Conv + ReLU + BatchNorm only); custom-ONNX-export engineering effort estimated at ~1-2 weeks vs ALIKED's ~4-6 weeks. **Two ADDITIONAL CAVEATS**: (v) MegaDepth-1500 sparse-mode AUC@5°=42.6 is materially below LightGlue-siblings (-7 to -25 absolute) at strictest tier — XFeat sparse positioned as "competitive at much higher speed" rather than "best-accuracy"; XFeat+LighterGlue paired-matcher narrows gap to -2.5 to -2.8 absolute below SP+LightGlue (Fast/Accurate configs); (vi) AERIAL-DOMAIN-TRAINING CAVEAT shared with all C3 candidates evaluated (D-C2-1 reuse with XFeat-cheapest-retrain advantage). **Three primary inference modes** (D-C3-6 NEW Plan-phase decision): (a) XFeat sparse with MNN matching for SIMPLEST deployment / (b) XFeat\* semi-dense with MNN+MLP-offset-refinement for HIGHEST inlier count per pair (1885 vs LightGlue 475 = 4× more inliers per pair per Source #81 Appendix F Table 6) / (c) XFeat+LighterGlue paired-matcher for MODERN learned-matcher accuracy with VerLab-trained LighterGlue ~3× faster than canonical LightGlue. **Documented Recall@K + cross-domain generalization advantage** — Source #81 paper Table 2 ScanNet-1500 indoor: XFeat outperforms ALL baselines including SuperPoint+DISK+ALIKE on indoor cross-domain transfer despite all methods being MegaDepth-trained (paper Appendix E hybrid-training generalization advantage). **D-C3-1 ALTERNATE-MODERN-COMPETITIVE-LEAD role** alongside DISK+LightGlue's RECOMMENDED-PRIMARY-MITIGATION — XFeat is the alternate cleanest license-compliant + structurally-different design point + materially-cheapest-retrain-cost C3 candidate. Carries D-C3-6 NEW (XFeat-mode-choice).

**Magic-Leap-restrictive-license track (HARD DISQUALIFIER for project deployment — mandatory-simple-baseline reference role only)**:

5. **SuperGlue+SuperPoint (mandatory-simple-baseline reference)** — Mandatory simple-baseline role per engine Component Option Breadth rule, structurally analogous to NetVLAD's role in C2 row. **NOT a Selected candidate path** for project deployment — three converging hard-disqualifiers: (i) **HARD LICENSE DISQUALIFIER** — Source #78 LICENSE byte-for-byte identical to Source #72 SuperPoint LICENSE = Magic Leap noncommercial-research-only SLA, blocks dual-use deployment; (ii) **TRAINING CODE NOT RELEASED** per Source #78 README — D-C2-1 retrain decision STRUCTURALLY BLOCKED; (iii) **4-10× SLOWER THAN LIGHTGLUE** per Source #71 paper §5 + Table 2 — Jetson Orin Nano Super extrapolation ~600-1200 ms per pair fp16 = catastrophic AC-4.1 FAIL even at K=1 pair/frame. **Documented Recall@K + AUC consistently 1-3 absolute below LightGlue across HPatches / MegaDepth / Aachen / IMC at 4-10× slower runtime per Source #71 paper Table 2 cross-cite**. **POSITIVE for the role**: establishes the long-established graph-neural-network sparse-matcher reference floor that DISK+LightGlue / SP+LightGlue / ALIKED+LightGlue / XFeat must measurably exceed at deployment-ready license + Jetson-friendly runtime + retrain-capable training — confirms the engine Component Option Breadth rule's purpose. **NO new Plan-phase decision raised** by SuperGlue+SuperPoint closure (three converging hard-disqualifiers make it NOT a Selected candidate path regardless of D-C1-1 license-posture choice).

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