3D object detection serves as the core basis of the perception tasks in autonomous driving. Recent years have seen the rapid progress of multi-modal fusion strategies for more robust and accurate 3D object detection. However, current researches for robust fusion are all learning-based frameworks, which demand a large amount of training data and are inconvenient to implement in new scenes. In this paper, we propose GOOD, a general optimization-based fusion framework that can achieve satisfying detection without training additional models and is available for any combinations of 2D and 3D detectors to improve the accuracy and robustness of 3D detection. First we apply the mutual-sided nearest-neighbor probability model to achieve the 3D-2D data association. Then we design an optimization pipeline that can optimize different kinds of instances separately based on the matching result. Apart from this, the 3D MOT method is also introduced to enhance the performance aided by previous frames. To the best of our knowledge, this is the first optimization-based late fusion framework for multi-modal 3D object detection which can be served as a baseline for subsequent research. Experiments on both nuScenes and KITTI datasets are carried out and the results show that GOOD outperforms by 9.1\% on mAP score compared with PointPillars and achieves competitive results with the learning-based late fusion CLOCs.

翻译：三维目标检测是自动驾驶感知任务的核心基础。近年来，多模态融合策略在实现更鲁棒、更精准的三维目标检测方面取得了快速发展。然而，当前针对鲁棒融合的研究均为基于学习的框架，这类方法需要大量训练数据且在新场景中部署不便。本文提出GOOD——一种通用优化融合框架，无需额外训练模型即可达到令人满意的检测效果，并能与任意二维/三维检测器组合提升三维检测的精度与鲁棒性。首先，我们采用互惠最近邻概率模型实现三维-二维数据关联；随后设计基于匹配结果的优化流水线，对不同类型实例进行差异化优化。此外，本文引入三维多目标跟踪方法，借助历史帧信息增强检测性能。据我们所知，这是首个基于优化的多模态三维目标检测后融合框架，可作为后续研究基线。我们在nuScenes与KITTI数据集上开展实验，结果表明：相较于PointPillars，GOOD在mAP指标上提升9.1%，且与基于学习的后融合方法CLOCs相比取得竞争性结果。