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The popular object detection metric 3D Average Precision (3D AP) relies onthe intersection over union between predicted bounding boxes and ground truthbounding boxes. However, depth estimation based on cameras has limitedaccuracy, which may cause otherwise reasonable predictions that suffer fromsuch longitudinal localization errors to be treated as false positives andfalse negatives. We therefore propose variants of the popular 3D AP metric thatare designed to be more permissive with respect to depth estimation errors.Specifically, our novel longitudinal error tolerant metrics, LET-3D-AP andLET-3D-APL, allow longitudinal localization errors of the predicted boundingboxes up to a given tolerance. The proposed metrics have been used in the WaymoOpen Dataset 3D Camera-Only Detection Challenge. We believe that they willfacilitate advances in the field of camera-only 3D detection by providing moreinformative performance signals.

LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection