Realizing unified monocular 3D object detection, including both indoor and outdoor scenes, holds great importance in applications like robot navigation. However, involving various scenarios of data to train models poses challenges due to their significantly different characteristics, e.g., diverse geometry properties and heterogeneous domain distributions. To address these challenges, we build a detector based on the bird's-eye-view (BEV) detection paradigm, where the explicit feature projection is beneficial to addressing the geometry learning ambiguity when employing multiple scenarios of data to train detectors. Then, we split the classical BEV detection architecture into two stages and propose an uneven BEV grid design to handle the convergence instability caused by the aforementioned challenges. Moreover, we develop a sparse BEV feature projection strategy to reduce computational cost and a unified domain alignment method to handle heterogeneous domains. Combining these techniques, a unified detector UniMODE is derived, which surpasses the previous state-of-the-art on the challenging Omni3D dataset (a large-scale dataset including both indoor and outdoor scenes) by 4.9% AP_3D, revealing the first successful generalization of a BEV detector to unified 3D object detection.

翻译：实现包含室内和室外场景的统一单目三维目标检测，在机器人导航等应用中具有重要意义。然而，利用多种场景数据训练模型面临显著特性差异带来的挑战，例如几何属性的多样性及异构领域分布。为解决这些问题，我们基于鸟瞰图（BEV）检测范式构建检测器，其显式特征投影有助于解决多场景数据训练时产生的几何学习歧义。随后，我们将经典BEV检测架构拆分为两阶段，并提出非均匀BEV网格设计以处理上述挑战引发的收敛不稳定问题。此外，我们开发了稀疏BEV特征投影策略以降低计算成本，以及统一领域对齐方法以处理异构领域。结合这些技术，我们推导出统一检测器UniMODE，在具有挑战性的Omni3D数据集（包含室内和室外场景的大规模数据集）上，以4.9%的AP_3D超越先前最先进方法，首次揭示了将BEV检测器成功泛化至统一三维目标检测的可行性。