In this paper, we raise a new issue on Unidentified Foreground Object (UFO)
detection in 3D point clouds, which is a crucial technology in autonomous
driving in the wild. UFO detection is challenging in that existing 3D object
detectors encounter extremely hard challenges in both 3D localization and
Out-of-Distribution (OOD) detection. To tackle these challenges, we suggest a
new UFO detection framework including three tasks: evaluation protocol,
methodology, and benchmark. The evaluation includes a new approach to measure
the performance on our goal, i.e. both localization and OOD detection of UFOs.
The methodology includes practical techniques to enhance the performance of our
goal. The benchmark is composed of the KITTI Misc benchmark and our additional
synthetic benchmark for modeling a more diverse range of UFOs. The proposed
framework consistently enhances performance by a large margin across all four
baseline detectors: SECOND, PointPillars, PV-RCNN, and PartA2, giving insight
for future work on UFO detection in the wild.

提出了一个针对 3D 点云中未知前景对象（UFO）检测的新问题，该问题是野外自动驾驶中至关重要的技术。通过建立一个包括评估协议、方法论和基准的新的 UFO 检测框架，解决了现有 3D 对象检测器在 3D 定位和超出分布（OOD）检测方面遇到的极大挑战。该框架在 KITTI Misc 基准和额外合成基准上通过四个基线检测器（SECOND、PointPillars、PV-RCNN、PartA2）跨越性能提升了一个较大的幅度，并为未来的野外 UFO 检测工作提供了新的见解。