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.

翻译：论文摘要：本文提出了三维点云中未识别前景目标（UFO）检测这一新课题，该技术对野外环境下的自动驾驶至关重要。UFO检测的难点在于，现有三维目标检测器在三维定位和分布外（OOD）检测两个方面均面临极大挑战。为攻克这些难题，我们提出了一套包含评估协议、方法学和基准测试三大任务的新型UFO检测框架。评估部分引入了一种衡量目标性能的新方法，即同时实现UFO的定位与OOD检测。方法学部分包含了提升目标性能的实用技术。基准测试由KITTI Misc基准测试和我们额外构建的合成基准测试组成，旨在模拟更多样化的UFO场景。该框架在SECOND、PointPillars、PV-RCNN和PartA2四种基线检测器上均显著提升了性能，为未来野外UFO检测研究提供了启示。