通过算法与数据统一迈向统一的3D目标检测
Towards Unified 3D Object Detection via Algorithm and Data Unification
AI总结：
本文从算法和数据角度解决室内外统一3D目标检测的挑战，提出基于BEV的两阶段单目检测器UniMODE及多模态检测器MM-UniMODE，并构建了首个统一多模态3D检测基准MM-Omni3D。
摘要：
实现统一的3D目标检测（包括室内和室外场景）在机器人导航等应用中具有重要意义。然而，由于各种场景的数据具有显著不同的特征（例如多样的几何属性和异构的域分布），引入各种场景的数据来训练模型带来了挑战。在这项工作中，我们提出从算法和数据两个角度来解决这些挑战。在算法方面，我们首先基于鸟瞰图（BEV）检测范式构建了一个单目3D目标检测器，其中显式特征投影有助于解决几何学习歧义。在该检测器中，我们将经典的BEV检测架构分为两个阶段，并提出了一种不均匀的BEV网格设计，以处理由场景间几何差异引起的收敛不稳定性。此外，我们开发了一种稀疏BEV特征投影策略以降低计算成本，并开发了一种统一的域对齐方法来处理异构域。从数据角度来看，我们提出结合深度信息以提高训练鲁棒性。具体而言，我们构建了第一个统一的多模态3D目标检测基准MM-Omni3D，并将上述单目检测器扩展为其多模态版本，这是第一个统一的多模态3D目标检测器。我们将设计的单目和多模态检测器分别命名为UniMODE和MM-UniMODE。实验结果揭示了几项凸显多模态数据优势的深刻发现，并证实了所有提出策略的有效性。
英文摘要：
Realizing unified 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 distinct characteristics, \eg, diverse geometry properties and heterogeneous domain distributions. In this work, we propose to address the challenges from two perspectives, the algorithm perspective and data perspective. In terms of the algorithm perspective, we first build a monocular 3D object detector based on the bird's-eye-view (BEV) detection paradigm, where the explicit feature projection is beneficial to addressing the geometry learning ambiguity. In this detector, we split the classical BEV detection architecture into two stages and propose an uneven BEV grid design to handle the convergence instability caused by geometry difference between scenarios. Besides, we develop a sparse BEV feature projection strategy to reduce the computational cost and a unified domain alignment method to handle heterogeneous domains. From the data perspective, we propose to incorporate depth information to improve training robustness. Specifically, we build the first unified multi-modal 3D object detection benchmark MM-Omni3D and extend the aforementioned monocular detector to its multi-modal version, which is the first unified multi-modal 3D object detector. We name the designed monocular and multi-modal detectors as UniMODE and MM-UniMODE, respectively. The experimental results reveal several insightful findings highlighting the benefits of multi-modal data and confirm the effectiveness of all the proposed strategies.
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