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729628a fb23187 729628a fb23187 729628a fb23187 729628a fb23187 729628a fb23187 729628a fb23187 729628a fb23187 729628a fb23187 729628a fb23187 729628a fb23187 729628a fb23187 729628a fb23187 729628a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | import Table from '@mui/material/Table';
import TableBody from '@mui/material/TableBody';
import TableCell from '@mui/material/TableCell';
import TableHead from '@mui/material/TableHead';
import TableRow from '@mui/material/TableRow';
import Typography from '@mui/material/Typography';
import type { QualityMetrics } from '../../types/labeleval';
import { SectionCard, fmtNum } from './shared';
import { Term } from '../help/InfoTip';
interface CheckRow {
check: string;
tolerance: string;
observed: (m: QualityMetrics) => string;
/** Glossary key explaining the check on hover. */
term?: string;
}
/**
* The geometric validation checks with their configured tolerances.
* The API only exposes aggregate metrics, so the "observed" column surfaces
* the closest aggregate where one exists.
*/
const CHECKS: CheckRow[] = [
{
check: 'Point-in-box ratio',
tolerance: '≥ 0.60 of box volume occupied by LiDAR returns',
observed: () => '— (per-annotation, see evidence panels)',
term: 'point_in_box_ratio',
},
{
check: 'Point density',
tolerance: '≥ 5 pts/m³ at range-adjusted threshold',
observed: () => '— (per-annotation, see evidence panels)',
term: 'point_density',
},
{
check: 'Occupancy',
tolerance: 'No overlap > 0.30 IoU with another box of same class',
observed: (m) => `mean 3D IoU ${fmtNum(m.global.mean_iou_3d)}`,
term: 'iou_3d',
},
{
check: 'Dimensions',
tolerance: 'Within class prior ± 2.5σ (l, w, h)',
observed: () => '— (per-annotation, see evidence panels)',
term: 'dimension_error',
},
{
check: 'Orientation',
tolerance: '≤ 15° error vs. reference / motion heading',
observed: (m) => `mean ${fmtNum(m.global.mean_orientation_error_deg, 1)}°`,
term: 'orientation_error',
},
{
check: 'Centroid',
tolerance: '≤ 0.5 m position error vs. reference',
observed: (m) => `mean ${fmtNum(m.global.mean_position_error)} m`,
term: 'position_error',
},
{
check: 'Ground contact',
tolerance: 'Box bottom within ± 0.3 m of ground plane',
observed: () => '— (per-annotation, see evidence panels)',
term: 'ground_contact',
},
{
check: 'Sensor consistency',
tolerance: 'LiDAR vs. camera class/extent agreement required',
observed: (m) => `anomaly rate ${(m.global.anomaly_rate * 100).toFixed(1)}%`,
term: 'sensor_consistency',
},
];
export default function StrictQualityValidation({ metrics }: { metrics: QualityMetrics | null }) {
return (
<SectionCard
title="Strict Quality Validation — geometric gates"
help="GT-free validation: every label is checked against physics and sensor evidence, no reference truth needed. Hover a check name for what it measures. Tolerances come from the active quality policy; per-annotation verdicts appear in each label's evidence panel and drive the geometric_validation gate in Triage."
>
<Typography variant="body2" sx={{ color: '#8a949e', mb: 1 }}>
Every annotation must pass all applicable geometric checks. Failing any single gate flags the label — gates are
never averaged away.
</Typography>
<Table size="small">
<TableHead>
<TableRow>
<TableCell>Check</TableCell>
<TableCell>Configured tolerance</TableCell>
<TableCell align="right">Observed aggregate</TableCell>
</TableRow>
</TableHead>
<TableBody>
{CHECKS.map((c) => (
<TableRow key={c.check} hover>
<TableCell sx={{ fontWeight: 600 }}>
{c.term ? <Term k={c.term}>{c.check}</Term> : c.check}
</TableCell>
<TableCell sx={{ color: '#aab4be', fontSize: 12 }}>{c.tolerance}</TableCell>
<TableCell align="right" sx={{ fontFamily: 'monospace', fontSize: 12 }}>
{metrics ? c.observed(metrics) : '—'}
</TableCell>
</TableRow>
))}
</TableBody>
</Table>
</SectionCard>
);
}
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