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

为了弥补基于相机的深度估计的限制性精度对3D Average Precision（3D AP）度量的影响，我们提出了 LET-3D-AP 和 LET-3D-APL 两种更加容错的度量方法，允许预测边界框的纵向定位误差达到一定的容忍度，以提供更多有信息量的性能信号。

LET-3D-AP：长纵向误差容忍的仅基于摄像头的3D检测的3D平均精度