Widely-used LiDAR-based 3D object detectors often neglect fundamental geometric information readily available from the object proposals in their confidence estimation. This is mostly due to architectural design choices, which were often adopted from the 2D image domain, where geometric context is rarely available. In 3D, however, considering the object properties and its surroundings in a holistic way is important to distinguish between true and false positive detections, e.g. occluded pedestrians in a group. To address this, we present GACE, an intuitive and highly efficient method to improve the confidence estimation of a given black-box 3D object detector. We aggregate geometric cues of detections and their spatial relationships, which enables us to properly assess their plausibility and consequently, improve the confidence estimation. This leads to consistent performance gains over a variety of state-of-the-art detectors. Across all evaluated detectors, GACE proves to be especially beneficial for the vulnerable road user classes, i.e. pedestrians and cyclists.

翻译：广泛使用的基于LiDAR的3D目标检测器在其置信度估计中往往忽略了从目标提议中容易获取的基础几何信息。这主要源于架构设计选择——这些设计通常从缺乏几何上下文的2D图像领域迁移而来。然而在3D场景中，需要全面考虑目标属性及其周边环境以区分真阳性检测与假阳性检测（例如群体中被遮挡的行人）。针对该问题，我们提出GACE——一种直观且高效的方法，用于提升给定黑盒3D目标检测器的置信度估计性能。通过整合检测结果的几何线索及其空间关联关系，该方法能够合理评估检测结果的合理性，进而优化置信度估计。实验表明，该方法在多种先进检测器上均能取得一致的性能提升。在所有评估的检测器中，GACE对弱势道路使用者类别（即行人与骑行者）的改进尤为显著。