Current 3D object detection models follow a single dataset-specific training
and testing paradigm, which often faces a serious detection accuracy drop when
they are directly deployed in another dataset. In this paper, we study the task
of training a unified 3D detector from multiple datasets. We observe that this
appears to be a challenging task, which is mainly due to that these datasets
present substantial data-level differences and taxonomy-level variations caused
by different LiDAR types and data acquisition standards. Inspired by such
observation, we present a Uni3D which leverages a simple data-level correction
operation and a designed semantic-level coupling-and-recoupling module to
alleviate the unavoidable data-level and taxonomy-level differences,
respectively. Our method is simple and easily combined with many 3D object
detection baselines such as PV-RCNN and Voxel-RCNN, enabling them to
effectively learn from multiple off-the-shelf 3D datasets to obtain more
discriminative and generalizable representations. Experiments are conducted on
many dataset consolidation settings including Waymo-nuScenes, nuScenes-KITTI,
Waymo-KITTI, and Waymo-nuScenes-KITTI consolidations. Their results demonstrate
that Uni3D exceeds a series of individual detectors trained on a single
dataset, with a 1.04x parameter increase over a selected baseline detector. We
expect this work will inspire the research of 3D generalization since it will
push the limits of perceptual performance.

该论文研究了如何训练一个来自多个数据集的统一 3D 检测器，提出了一种名为 Uni3D 的方法来解决数据级别和分类学级别的差异，证明了该方法的有效性并对进一步的 3D 泛化研究具有启发意义。