3D visual perception tasks based on multi-camera images are essential for autonomous driving systems. Latest work in this field performs 3D object detection by leveraging multi-view images as an input and iteratively enhancing object queries (object proposals) by cross-attending multi-view features. However, individual backbone features are not updated with multi-view features and it stays as a mere collection of the output of the single-image backbone network. Therefore we propose 3M3D: A Multi-view, Multi-path, Multi-representation for 3D Object Detection where we update both multi-view features and query features to enhance the representation of the scene in both fine panoramic view and coarse global view. Firstly, we update multi-view features by multi-view axis self-attention. It will incorporate panoramic information in the multi-view features and enhance understanding of the global scene. Secondly, we update multi-view features by self-attention of the ROI (Region of Interest) windows which encodes local finer details in the features. It will help exchange the information not only along the multi-view axis but also along the other spatial dimension. Lastly, we leverage the fact of multi-representation of queries in different domains to further boost the performance. Here we use sparse floating queries along with dense BEV (Bird's Eye View) queries, which are later post-processed to filter duplicate detections. Moreover, we show performance improvements on nuScenes benchmark dataset on top of our baselines.

翻译：基于多相机图像的三维视觉感知任务是自动驾驶系统的核心需求。该领域最新工作通过将多视角图像作为输入，并利用交叉注意力机制迭代增强目标查询（目标提议）来实现三维目标检测。然而，各主干网络提取的特征并未与多视角特征进行交互更新，仍停留在单幅图像主干网络输出的简单集合状态。为此我们提出3M3D：一种融合多视角、多路径、多表示的三维目标检测方法，通过同时更新多视角特征与查询特征来增强场景表示，兼顾精细全景视图与粗粒度全局视图。首先，采用多视角轴向自注意力机制更新多视角特征，将全景信息融入多视角特征以提升对全局场景的理解；其次，通过感兴趣区域（ROI）窗口的自注意力机制更新多视角特征，编码特征中的局部细节信息，实现不仅沿多视角轴向，还沿其他空间维度的信息交换；最后，利用不同域中查询的多表示特性进一步提升性能，具体采用稀疏浮点查询与稠密鸟瞰图（BEV）查询相结合的方式，并通过后处理过滤重复检测结果。在nuScenes基准数据集上的实验表明，本方法在基线模型基础上取得了显著的性能提升。