DW: Detected weight for 3D object detection

It is a generic paradigm to treat all samples equally in 3D object detection. Although some works focus on discriminating samples in the training process of object detectors, the issue of whether a sample detects its target GT (Ground Truth) during training process has never been studied. In this work, we first point out that discriminating the samples that detect their target GT and the samples that don’t detect their target GT is beneficial to improve the performance measured in terms of mAP (mean Average Precision). Then we propose a novel approach name as DW (Detected Weight). The proposed approach dynamically calculates and assigns different weights to detected and undetected samples, which suppresses the former and promotes the latter. The approach is simple, low-calculation and can be integrated with available weight approaches. Further, it can be applied to almost 3D detectors, even 2D detectors because it is nothing to do with network structures. We evaluate the proposed approach with six state-of-the-art 3D detectors on two datasets. The experiment results show that the proposed approach improves mAP significantly.