UFO: Unidentified Foreground Object Detection in 3D Point Cloud
Hyunjun Choi Hawook Jeong Jin Young Choi
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.