Recent studies have focused on enhancing the performance of 3D object detection models. Among various approaches, ground-truth sampling has been proposed as an augmentation technique to address the challenges posed by limited ground-truth data. However, an inherent issue with ground-truth sampling is its tendency to increase false positives. Therefore, this study aims to overcome the limitations of ground-truth sampling and improve the performance of 3D object detection models by developing a new augmentation technique called false-positive sampling. False-positive sampling involves retraining the model using point clouds that are identified as false positives in the model's predictions. We propose an algorithm that utilizes both ground-truth and false-positive sampling and an algorithm for building the false-positive sample database. Additionally, we analyze the principles behind the performance enhancement due to false-positive sampling and propose a technique that applies the concept of curriculum learning to the sampling strategy that encompasses both false-positive and ground-truth sampling techniques. Our experiments demonstrate that models utilizing false-positive sampling show a reduction in false positives and exhibit improved object detection performance. On the KITTI and Waymo Open datasets, models with false-positive sampling surpass the baseline models by a large margin.

翻译：近期研究聚焦于提升3D目标检测模型的性能。在众多方法中，真值采样被提出作为一种增强技术，以应对有限真值数据带来的挑战。然而，真值采样存在一个固有问题，即容易增加假阳性检测结果。因此，本研究旨在克服真值采样的局限性，通过开发一种名为假阳性采样的新型增强技术来提升3D目标检测模型的性能。假阳性采样方法利用模型预测中被识别为假阳性的点云数据对模型进行重新训练。我们提出了一种同时运用真值采样与假阳性采样的算法，以及构建假阳性样本数据库的算法。此外，我们分析了假阳性采样提升性能的内在原理，并提出了一种将课程学习理念应用于融合假阳性采样与真值采样策略的增强技术。实验结果表明，采用假阳性采样的模型在减少假阳性检测结果的同时，展现了更优的目标检测性能。在KITTI和Waymo Open数据集上，采用假阳性采样的模型在性能上大幅超越了基准模型。