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.

在广泛使用的基于 LiDAR 的三维物体检测中，由于架构设计选择通常是从二维图像领域采用，很容易忽略从物体提案中获得的基本几何信息。而在三维中，综合考虑物体属性和其周围环境对于区分真伪正检测是重要的，为此我们提出了 GACE，这是一种直观高效的方法来改善给定黑盒三维物体检测器的置信度估计，在各种先进检测器中表现出相对一致的性能提升。在所有评估的检测器中，GACE 在易受伤害的道路使用者类别（即行人和骑车人）中表现尤为有益。