We present Uni$^2$Det, a brand new framework for unified and universal multi-dataset training on 3D detection, enabling robust performance across diverse domains and generalization to unseen domains. Due to substantial disparities in data distribution and variations in taxonomy across diverse domains, training such a detector by simply merging datasets poses a significant challenge. Motivated by this observation, we introduce multi-stage prompting modules for multi-dataset 3D detection, which leverages prompts based on the characteristics of corresponding datasets to mitigate existing differences. This elegant design facilitates seamless plug-and-play integration within various advanced 3D detection frameworks in a unified manner, while also allowing straightforward adaptation for universal applicability across datasets. Experiments are conducted across multiple dataset consolidation scenarios involving KITTI, Waymo, and nuScenes, demonstrating that our Uni$^2$Det outperforms existing methods by a large margin in multi-dataset training. Notably, results on zero-shot cross-dataset transfer validate the generalization capability of our proposed method.

本研究针对多数据集3D检测训练中的数据分布差异与分类法不一致的问题，提出了新的框架Uni$^2$Det。通过引入多阶段提示模块，研究利用各数据集特征的提示来缩小差距，从而大幅提高了在多数据集训练中的检测性能，并在零样本跨数据集迁移上证明了该方法的良好泛化能力。

Uni$^2$Det：统一通用的提示引导多数据集3D检测框架