Supervised 3D Object Detection models have been displaying increasingly better performance in single-domain cases where the training data comes from the same environment and sensor as the testing data. However, in real-world scenarios data from the target domain may not be available for finetuning or for domain adaptation methods. Indeed, 3D object detection models trained on a source dataset with a specific point distribution have shown difficulties in generalizing to unseen datasets. Therefore, we decided to leverage the information available from several annotated source datasets with our Multi-Dataset Training for 3D Object Detection (MDT3D) method to increase the robustness of 3D object detection models when tested in a new environment with a different sensor configuration. To tackle the labelling gap between datasets, we used a new label mapping based on coarse labels. Furthermore, we show how we managed the mix of datasets during training and finally introduce a new cross-dataset augmentation method: cross-dataset object injection. We demonstrate that this training paradigm shows improvements for different types of 3D object detection models. The source code and additional results for this research project will be publicly available on GitHub for interested parties to access and utilize: https://github.com/LouisSF/MDT3D

翻译：监督式三维目标检测模型在单一领域场景下（即训练数据与测试数据来自相同环境和传感器）已展现出越来越优异的性能。然而，在现实场景中，目标域的数据可能无法用于微调或领域自适应方法。实际上，在特定点云分布的源数据集上训练的三维目标检测模型，在泛化至未见数据集时存在困难。为此，我们提出多数据集三维目标检测训练方法（MDT3D），利用多个已标注源数据集的信息，增强模型在具有不同传感器配置的新环境中测试时的鲁棒性。为解决数据集间的标注差异，我们基于粗粒度标签构建了新的标签映射关系。此外，我们阐述了训练过程中数据集混合的管理策略，并最终引入了一种新的跨数据集增强方法：跨数据集目标注入。实验证明，该训练范式可显著提升不同类型三维目标检测模型的性能。本研究的源代码及其他结果将发布于GitHub（https://github.com/LouisSF/MDT3D），供相关领域者访问使用。