vision3d.metrics#

3D object detection evaluation metrics.

Classes

APInterpolation(*values)

AP interpolation mode.

MeanAveragePrecision3D(class_ids[, ...])

3D detection mAP metric.

MeanAveragePrecision3DResult

Structured result returned by MeanAveragePrecision3D.compute().

Prediction3D

Per-frame detection output.

Target3D

Per-frame ground-truth annotations.
class vision3d.metrics.APInterpolation(*values)[source]#

Bases: Enum

AP interpolation mode.

R40 = 'r40'#

40-point interpolation (modern KITTI default).

R11 = 'r11'#

11-point interpolation (legacy KITTI, PASCAL VOC07).

R101 = 'r101'#

101-point interpolation (COCO).

ALL_POINTS = 'all_points'#

VOC07 area-under-curve at every recall change.

class vision3d.metrics.MeanAveragePrecision3D(class_ids, iou_thresholds=(0.5, 0.7), ap_interpolation=APInterpolation.R40, range_bins=None)[source]#

Bases: object

3D detection mAP metric.

Matching is greedy by descending score, one prediction to one ground truth, with precision/recall accumulated globally across frames (KITTI convention).

Parameters:

  • class_ids (list[int]) – Integer class IDs to score. Predictions and GTs with labels outside this set are ignored.

  • iou_thresholds (tuple[float, ...]) – IoU thresholds to report AP at. Default (0.5, 0.7).

  • ap_interpolation (APInterpolation) – Interpolation mode. Default APInterpolation.R40.

  • range_bins (tuple[tuple[float, float], ...] | None) – Optional distance bins [low, high) in meters from the sensor origin. When set, AP is also broken down per bin; boxes are bucketed by their center’s distance.

update(preds, targets)[source]#

Accumulate one or more frames of predictions vs ground truth.

Parameters:
Raises:

ValueError – If preds and targets differ in length.

Return type:

None

compute()[source]#

Compute the aggregated metric.

Returns:

Populated MeanAveragePrecision3DResult.

Return type:

MeanAveragePrecision3DResult

reset()[source]#

Clear all accumulated state.

Return type:

None

class vision3d.metrics.MeanAveragePrecision3DResult[source]#

Bases: TypedDict

Structured result returned by MeanAveragePrecision3D.compute().

Undefined slots (buckets with no ground-truth boxes accumulated) are reported as -1.0 and callers can filter them with x >= 0.

Variables:

  • mAP (float) – Overall mean AP, taken over every defined (class, iou, bin) bucket.

  • mAP_per_class (dict[int, float]) – AP per class, averaged over the other axes.

  • AP_per_iou (dict[float, float]) – AP per IoU threshold, averaged over the other axes.

  • AP_per_class_per_iou (dict[tuple[int, float], float]) – AP per (class, iou) pair, averaged over range bins (or a single value when range bucketing is disabled).

  • AP_per_range (dict[tuple[float, float], float]) – AP per range bin, averaged over the other axes. Only present when range_bins was set on the metric.

  • AP_per_class_per_range (dict[tuple[int, tuple[float, float]], float]) – AP per (class, range_bin) pair, averaged over IoU thresholds. Only present when range_bins was set on the metric.

class vision3d.metrics.Prediction3D[source]#

Bases: TypedDict

Per-frame detection output.

Variables:
class vision3d.metrics.Target3D[source]#

Bases: TypedDict

Per-frame ground-truth annotations.

Variables: