论文标题
svga-net：从点云中检测3D对象检测的稀疏体素图网络
SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds
论文作者
论文摘要
从点云中准确的3D对象检测已成为自动驾驶中的关键组成部分。但是，以前的作品中的体积表示和投影方法无法建立本地点集之间的关系。在本文中，我们提出了一种稀疏的体素电段注意网络（SVGA-NET），这是一个新型的端到端可训练网络，主要包含Voxel-Graph模块和稀疏到密度的回归模块，以实现来自RAW LIDAR数据的可比3D检测任务。具体而言，SVGA-NET通过所有体素构建了每个分隔的3D球形体素和全局KNN图内的本地完整图。本地和全球图是增强提取特征的注意机制。此外，新型稀疏到密度的回归模块通过在不同级别的特征地图聚集来提高3D框估计精度。 KITTI检测基准的实验证明了将图表扩展到3D对象检测的效率，并且提出的SVGA-NET可以达到不错的检测准确性。
Accurate 3D object detection from point clouds has become a crucial component in autonomous driving. However, the volumetric representations and the projection methods in previous works fail to establish the relationships between the local point sets. In this paper, we propose Sparse Voxel-Graph Attention Network (SVGA-Net), a novel end-to-end trainable network which mainly contains voxel-graph module and sparse-to-dense regression module to achieve comparable 3D detection tasks from raw LIDAR data. Specifically, SVGA-Net constructs the local complete graph within each divided 3D spherical voxel and global KNN graph through all voxels. The local and global graphs serve as the attention mechanism to enhance the extracted features. In addition, the novel sparse-to-dense regression module enhances the 3D box estimation accuracy through feature maps aggregation at different levels. Experiments on KITTI detection benchmark demonstrate the efficiency of extending the graph representation to 3D object detection and the proposed SVGA-Net can achieve decent detection accuracy.
