论文标题
中心形式：用于3D对象检测的基于中心的变压器
CenterFormer: Center-based Transformer for 3D Object Detection
论文作者
论文摘要
基于查询的变压器在许多图像域任务中构建长期注意力方面表现出巨大的潜力，但是由于点云数据的压倒性大小，在基于激光雷达的3D对象检测中很少考虑。在本文中，我们提出了CenterFormer，这是一种基于中心的变压器网络，用于3D对象检测。 CenterFormer首先使用中心热图来选择标准基于体素的点云编码器顶部的中心候选者。然后，它将中心候选者的功能用作变压器中的查询嵌入。为了进一步从多个帧中汇总功能，我们通过交叉注意设计一种方法来融合功能。最后，添加回归头以预测输出中心特征表示形式上的边界框。我们的设计降低了变压器结构的收敛难度和计算复杂性。结果表明，与无锚对象检测网络的强基线相比。 CenterFormer在Waymo Open数据集上实现了单个模型的最新性能，验证集的MAPH为73.7％，测试集的MAPH上有75.6％的MAPH，大大优于所有先前发布的CNN和基于变压器的方法。我们的代码可在https://github.com/tusimple/centerformer上公开获取
Query-based transformer has shown great potential in constructing long-range attention in many image-domain tasks, but has rarely been considered in LiDAR-based 3D object detection due to the overwhelming size of the point cloud data. In this paper, we propose CenterFormer, a center-based transformer network for 3D object detection. CenterFormer first uses a center heatmap to select center candidates on top of a standard voxel-based point cloud encoder. It then uses the feature of the center candidate as the query embedding in the transformer. To further aggregate features from multiple frames, we design an approach to fuse features through cross-attention. Lastly, regression heads are added to predict the bounding box on the output center feature representation. Our design reduces the convergence difficulty and computational complexity of the transformer structure. The results show significant improvements over the strong baseline of anchor-free object detection networks. CenterFormer achieves state-of-the-art performance for a single model on the Waymo Open Dataset, with 73.7% mAPH on the validation set and 75.6% mAPH on the test set, significantly outperforming all previously published CNN and transformer-based methods. Our code is publicly available at https://github.com/TuSimple/centerformer
