Locus Release Performance Report (2026-04-18)
Historical snapshot for Locus v0.3.1. Numbers are stale (superseded by
render_tag_sota_20260713.md and later reports); kept for the
kernel-speedup trend and as the origin of the PPB investigation now
summarized in lessons.md §4.4.
Tier 1: End-to-End Performance (Python CLI)
Validated on the ICRA 2020 dataset and synthetic targets.
Measurements taken with RAYON_NUM_THREADS=1.
Real-World (ICRA 2020 - Scenario: forward)
Measured on all 50 images of the forward/pure_tags_images subset.
| Configuration | Recall | RMSE (px) | Latency (ms) |
|---|---|---|---|
| Locus (Soft) | 96.23% | 0.3152 | 151.12 |
| Locus (Hard) | 76.87% | 0.2567 | 92.24 |
Note: Python CLI latency includes significant FFI and result-marshalling overhead. Rust core latencies for the same workload are ~32ms (Hard) and ~42ms (Soft).
Tier 2: Micro-Benchmarking (Divan)
Fine-grained mathematical kernels measured strictly single-threaded on 1080p images. Substantial improvements observed compared to the March 2026 baseline.
Mathematical Kernels (1080p)
| Kernel | Latest Median (ms) | Baseline (2026-03-19) | Speedup |
|---|---|---|---|
| Thresholding (Apply) | 1.51 ms | 4.26 ms | 2.82x |
| Segmentation | 2.57 ms | 2.95 ms | 1.15x |
| Quad Extraction | 23.79 ms | 34.35 ms | 1.44x |
Tier 3: Rust Regression Suite
ICRA 2020 (HighAccuracy Presets)
| Scenario | Preset | Recall | RMSE (px) | Latency (ms) |
|---|---|---|---|---|
| Forward | Standard (Soft) | 96.23% | 0.3152 | 41.81 |
| Forward | Grid | 91.43% | 0.4577 | 58.53 |
| Forward | HighAccuracy | 46.31% | 0.7535 | 15.43 |
Investigation: HighAccuracy Performance
HighAccuracy: 46.3% recall on ICRA 2020 forward vs. 95.6% on Hub 1080p —
root-caused to PPB (pixels-per-bit); full writeup and the resulting
per-PPB-band recommendation table now live in
lessons.md §4.4.
Hub Regression (Rendered Tags - 1080p Tag36h11)
| Mode | Recall | Trans P50 (mm) | Latency (ms) |
|---|---|---|---|
| Standard | 100.0% | 5.3 mm | 21.69 |
| HighAccuracy | 95.56% | 0.7 mm | 11.44 |
Technical Updates in this Version
- Snapshot Stabilization: All regression snapshots have been updated to established v0.3.1 baselines.
- Kernel Optimization: SIMD-accelerated thresholding and segmentation provide significant throughput gains.
- Metrology Handover: GN corner covariances now propagate directly to the weighted LM pose solver in HighAccuracy mode.