论文标题
GRAFOS：3D点云检测的体素选择
GraVoS: Voxel Selection for 3D Point-Cloud Detection
论文作者
论文摘要
大型3D场景中的3D对象检测不仅是由于3D点云的稀疏性和不规则性，而且还因为极端的前景背景场景不平衡和阶级不平衡所致。一种常见的方法是从其他场景中添加地面真实对象。不同的是，我们建议通过删除元素（体素）而不是添加元素来修改场景。我们的方法以解决两种类型的数据集不平衡的方式选择“有意义的”体素。该方法是一般的，可以应用于任何基于体素的检测器，但是体素的有意义依赖于网络。我们的体素选择显示可提高几种突出的3D检测方法的性能。
3D object detection within large 3D scenes is challenging not only due to the sparsity and irregularity of 3D point clouds, but also due to both the extreme foreground-background scene imbalance and class imbalance. A common approach is to add ground-truth objects from other scenes. Differently, we propose to modify the scenes by removing elements (voxels), rather than adding ones. Our approach selects the "meaningful" voxels, in a manner that addresses both types of dataset imbalance. The approach is general and can be applied to any voxel-based detector, yet the meaningfulness of a voxel is network-dependent. Our voxel selection is shown to improve the performance of several prominent 3D detection methods.
