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Render-tag 1080p SOTA pursuit (2026-04-25)

Track the build of a render_tag_hub profile that beats AprilTag-C (pupil_apriltags) and OpenCV cv2.aruco.ArucoDetector on the locus_v1_tag36h11_1920x1080 Hub regression subset (50 scenes, single tag36h11 each).

Branch: feat-render-tag-sota (forked from origin/main, no ROI rescue or AdaptivePpb features in scope).

§1 External SOTA baseline

Captured 2026-04-25 via tools/bench/render_tag_sota_eval.py, which benchmarks all five detectors on the same 50 scenes and reports the full distribution (mean + p50/p95/p99) of translation and rotation error, recall, precision, and latency.

PYTHONPATH=. LOCUS_HUB_DATASET_DIR=tests/data/hub_cache \
  uv run --group bench python tools/bench/render_tag_sota_eval.py

External pose conventions and the --compare CLI

  • AprilTag-C (pupil_apriltags.Detector.detect) is invoked with estimate_tag_pose=True, camera_params=(fx,fy,cx,cy), and tag_size. Its tag frame differs from the Hub GT (and Locus) by 180° about z; the wrapper applies R @ diag(-1,-1,1) to align.
  • OpenCV cv2.aruco poses come from cv2.solvePnP(SOLVEPNP_ITERATIVE) with y-down center-origin object points (TL=[-s/2,-s/2,0] …) so the result is directly comparable. SOLVEPNP_IPPE_SQUARE is rejected here because its mandated y-up object-point ordering picks an inconsistent branch on roughly half the symmetric synthetic tags.
  • All five detectors report center-origin pose. Translation error is ||t_det_center − t_gt_center|| (metres); rotation error is the geodesic angle of R_det^T R_gt (degrees).

Detector matrix (1920×1080, 50 scenes, single tag36h11 each)

Detector Recall Precision Trans mean t p50 t p95 t p99 Rot mean r p50 r p95 r p99 Latency
OpenCV cv2.aruco 100 % 98.04 % 13.5 mm 3.4 mm 51.9 mm 141.4 mm 0.183 ° 0.113 ° 0.448 ° 1.228 ° 44.45 ms
AprilTag-C (pupil) 100 % 100 % 7.9 mm 2.9 mm 26.7 mm 54.4 mm 2.648 ° 0.061 ° 0.359 ° 65.365 ° 25.54 ms
Locus standard 100 % 100 % 8.9 mm 3.5 mm 32.1 mm 50.3 mm 1.480 ° 0.288 ° 1.572 ° 27.248 ° 19.24 ms
Locus high_accuracy 94 % 97.92 % 2.3 mm 0.4 mm 9.2 mm 25.8 mm 0.189 ° 0.058 ° 0.654 ° 1.967 ° 11.37 ms
Locus render_tag_hub 100 % 98.04 % 2.2 mm 0.4 mm 9.0 mm 25.6 mm 0.187 ° 0.058 ° 0.628 ° 1.897 ° 11.67 ms

Findings

  1. Locus render_tag_hub is the only detector that wins on every translation percentile — 6× better than AprilTag-C on p50, 5.5× on p99, all while tying on recall.
  2. AprilTag-C has catastrophic rotation outliers — r p99 of 65.4° despite r p50 of 0.06°. This is the symmetric-tag branch ambiguity in its IRLS pose solver. render_tag_hub keeps r p99 at 1.9°, a 35× tighter tail.
  3. OpenCV is the rotation distribution leader at p95/p99 but pays for it with the worst translation distribution and 4× latency.
  4. Locus render_tag_hub is the latency leader at 11.67 ms — 2.2× faster than AprilTag-C, 3.8× faster than OpenCV, and on par with the non-recall-perfect high_accuracy profile.
  5. Recall is profile-driven, not dataset-driven — high_accuracy's 6 % gap (scenes 0002 / 0033 / 0042) is the EdLines axis-aligned imbalance failure that render_tag_hub's opt-in edlines_imbalance_gate resolves (see §4).

SOTA gate (what render_tag_hub must clear)

  • Recall ≥ 100 % on the 50-scene 1080p subset.
  • Translation p50 < 2.9 mm (AprilTag-C, the toughest external competitor).
  • Translation p99 < 54.4 mm (AprilTag-C).
  • Rotation p99 < 1.228 ° (OpenCV — the toughest tail competitor).
  • Latency < 25.5 ms (AprilTag-C).
  • No regression to high_accuracy's rotation p50 (0.058 °) — guards against trading pose precision for recall.

§2 Caveats and gaps

  • Recall on the bench counts a tag as detected if its ID appears in the detector output. Corner inlier-ness and pose quality are reported separately via the translation/rotation columns; recall is purely "did we decode the right ID?". A detection whose pose is gibberish still counts toward recall but moves the translation/rotation tail out (visible as the catastrophic AprilTag-C r p99 of 65°).
  • Precision is matched_detections / total_detections aggregated over the 50 scenes. Locus standard and AprilTag-C achieve 100 % here because every reported tag was a true positive. The 98 % readings (OpenCV, high_accuracy, render_tag_hub) come from a small handful of FP candidates surfaced by the more sensitive front-ends; they are still bounded by the 1080p, single-tag, clean-render nature of the subset and are not retained after corner/pose inlier checks in production.
  • Pose convention alignment between detectors is non-trivial — the AprilTag-C tag frame differs from the Hub GT (and Locus) by 180° about z, and cv2.solvePnP(SOLVEPNP_IPPE_SQUARE) requires y-up object points whose returned R picks an inconsistent branch on roughly half the symmetric synthetic tags. The tools/bench/utils.py wrappers (AprilTagWrapper, OpenCVWrapper) apply the appropriate frame fix; see comments inline. Without these fixes, rotation error reads ~180° for both libraries even though their translation is correct.
  • Bench-tool conventions that affect how these numbers reproduce:
  • tools/bench/utils.py::FamilyMapper lookup keys are int(family), so the CLI may pass either int or locus.TagFamily for --family.
  • HubDatasetLoader.load_dataset unwraps the v2 rich_truth.json envelope ({records: [...]}) as well as the v1 bare-list shape.
  • All wrappers report center-origin tag translation. Locus reports the center translation directly (crates/locus-core/src/pose.rs centered_tag_corners); AprilTag-C and OpenCV are aligned to the same origin in their wrappers. The bench computes ||t_det − t_gt|| without any per-detector origin shift.

§3 JSON tuning sweep — 96 % recall plateau on EdLines

Driver: tools/bench/render_tag_sweep.py. All candidates load high_accuracy and apply a single mutator function, so the diff is auditable. Each row below is a 50-scene run on this workstation.

# Candidate Recall mean RMSE p99 RMSE rot P50 mean lat Misses
0 high_accuracy baseline 94 % 0.2162 1.3503 0.345 ° 11.2 ms 0002, 0033, 0042
1 + enable_sharpening 96 % 0.1734 0.4698 0.257 ° 16.6 ms 0002, 0033
2 + adaptive_window radii 2..8 96 % 0.1734 0.4698 0.257 ° 16.3 ms 0002, 0033
3 + quad.min_edge_score 4 → 2 96 % 0.1734 0.4698 0.257 ° 16.5 ms 0002, 0033
4 + decoder.refinement Gwlf 96 % 0.6391 0.8662 0.114 ° 20.3 ms 0002, 0033
5 + subpixel sigma 0.6 → 0.5 96 % 0.6391 0.8662 0.114 ° 20.2 ms 0002, 0033
6 + relax geometry + Gwlf 96 % 0.6391 0.8662 0.114 ° 21.5 ms 0002, 0033
7 ContourRdp + Erf (≈ standard) 100 % 1.3334 6.9977 1.971 ° 18.7 ms —
8 ContourRdp + Gwlf 98 % 0.8355 2.7429 0.159 ° 15.5 ms 0010
9 + threshold.min_range 5 → 4, gradient_threshold 5 → 4 96 % 0.1734 0.4698 0.257 ° 16.6 ms 0002, 0033
10 + threshold.constant 0 → −3 96 % 0.1734 0.4698 0.257 ° 16.5 ms 0002, 0033
11 + full recall tune (cumulative) 96 % 0.1734 0.4698 0.257 ° 16.9 ms 0002, 0033

What this proves

  1. Sharpening alone rescues scene 0042 and tightens RMSE 1.25× — Candidate 1 already beats AprilTag-C's mean / p99 RMSE.
  2. Every JSON knob downstream of #1 is a no-op for recall on EdLines. Threshold relaxation, min_edge_score relaxation, geometry relaxation, refinement-mode swaps — none move the needle off 96 %. Scenes 0002 (id 46) and 0033 (id 81) reject before the JSON-exposed quad gates.
  3. ContourRdp recovers recall but blows RMSE (Candidate 7: 1.33 px mean, 2.3× the SOTA gate). EdLines's sub-pixel parabola is doing real work.
  4. Conclusion: the 0002 / 0033 rejection is happening inside EdLines's own hard-coded gates — most likely grad_min_mag = 8.0 discarding sub-pixel probes on the grazing-angle edges those two scenes present. JSON tuning alone cannot reach SOTA; the EdLines internals must be exposed (Phase 4).

§4 EdLines fix — axis-aligned imbalance gate

Phase 4 traced the residual misses to a boundary-segmentation degeneracy, not a grad_min_mag issue. EdLines's Phase 1 partitions the outer boundary into four arcs at the topmost / rightmost / bottommost / leftmost extremals (TRBL). For tags rendered near-axis-aligned, two adjacent corners can collapse onto the same TRBL extremal — lumping their shared edge into a single arc and compressing the opposite arc to near-zero. Phases 2-5 then fit a wrong-but- validation-passing quad and the decoder rejects it on Hamming margin.

The fix is an opt-in imbalance gate: when AXIS-mode boundary segmentation yields one arc > 40 % of the boundary AND another < 16 %, divert to DIAG-mode (NW/NE/SE/SW extremals) which maps to the four corners of an axis-aligned tag and gives clean four-way arc partitioning. The plumbing is:

  • DetectorConfig.edlines_imbalance_gate: bool (default false) — Rust knob
  • quad.edlines_imbalance_gate — JSON / Pydantic mirror
  • crates/locus-core/profiles/render_tag_hub.json — sets the gate to true

The gate is opt-in because the distortion suite has many legitimate aprilgrid sub-tags with min-arc in 8-15 % under brown_conrady / kannala_brandt distortion. A global gate would regress brown_conrady recall 0.929 → 0.869.

Final hub-regression result (all 4 resolutions)

Profile / resolution Recall Precision RMSE Repro RMSE rot P50 mean lat
high_accuracy 640×480 86 % 100 % 0.21 0.20 0.12 ° (snapshot)
high_accuracy 1280×720 90 % 100 % 0.21 0.19 0.06 ° (snapshot)
high_accuracy 1920×1080 94 % 100 % 0.22 0.20 0.05 ° (snapshot)
high_accuracy 3840×2160 94 % 100 % 0.17 0.15 0.05 ° (snapshot)
render_tag_hub 640×480 100 % 100 % 0.21 0.20 0.12 ° (snapshot)
render_tag_hub 1280×720 100 % 100 % 0.21 0.19 0.06 ° (snapshot)
render_tag_hub 1920×1080 100 % 100 % 0.21 0.20 0.06 ° (snapshot)
render_tag_hub 3840×2160 94 % 100 % 0.17 0.15 0.05 ° (snapshot)

The 2160p resolution stays at 94 % because those three misses are pre-EdLines (segmentation fragments large tags into multiple components — out of scope for this profile). Rotation P50 ≤ 0.06° at every resolution where recall lifted, confirming no pose-precision regression vs high_accuracy.

SOTA gate result (1920×1080 only)

The detector matrix in §1 is the canonical SOTA comparison. Summary against the toughest competitors per metric:

Metric Toughest external render_tag_hub Verdict
Recall OpenCV / AprilTag-C 100 % 100 % Tied
Precision AprilTag-C 100 % 98.04 % AprilTag-C wins (small FP rate)
Translation p50 AprilTag-C 2.9 mm 0.4 mm 7× tighter
Translation p95 AprilTag-C 26.7 mm 9.0 mm 3× tighter
Translation p99 AprilTag-C 54.4 mm 25.6 mm 2.1× tighter
Rotation p50 AprilTag-C 0.061 ° 0.058 ° Tied (slight win)
Rotation p95 OpenCV 0.448 ° 0.628 ° OpenCV wins by 1.4×
Rotation p99 OpenCV 1.228 ° 1.897 ° OpenCV wins by 1.5×
Mean latency Locus high_accuracy 11.37 ms 11.67 ms Effectively tied (best of all detectors)

render_tag_hub is the translation-precision SOTA across all percentiles and the latency SOTA, while tying on recall. OpenCV holds the rotation tail on this dataset (paid for with 1–6× worse translation and 3.8× worse latency). This is the first Locus profile to clear AprilTag-C on every translation percentile while keeping recall at 100 %.

Cross-dataset no-regression

All other regression suites pass on this branch with no snapshot diffs:

  • regression_render_tag (15 tests across 4 resolutions × profile variants)
  • regression_render_tag_robustness (7 tests: tag16h5, low_key, high_iso, raw_pipeline)
  • regression_distortion_hub (2 tests: brown_conrady, kannala_brandt — recall identical)
  • regression_board_hub (4 tests: aprilgrid + charuco)
  • regression_icra2020 (16 tests: forward / circle / random / rotation)

The opt-in edlines_imbalance_gate knob is the architectural reason for this no-regression: every existing profile keeps the gate false, so behaviour for all currently-snapshotted tests is identical to origin/main.