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Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation

2023-01-24 16:10:08
Gilles Puy, Alexandre Boulch, Renaud Marlet


Semantic segmentation of point clouds in autonomous driving datasets requires techniques that can process large numbers of points over large field of views. Today, most deep networks designed for this task exploit 3D sparse convolutions to reduce memory and computational loads. The best methods then further exploit specificities of rotating lidar sampling patterns to further improve the performance, e.g., cylindrical voxels, or range images (for feature fusion from multiple point cloud representations). In contrast, we show that one can build a well-performing point-based backbone free of these specialized tools. This backbone, WaffleIron, relies heavily on generic MLPs and dense 2D convolutions, making it easy to implement, and contains just a few parameters easy to tune. Despite its simplicity, our experiments on SemanticKITTI and nuScenes show that WaffleIron competes with the best methods designed specifically for these autonomous driving datasets. Hence, WaffleIron is a strong, easy-to-implement, baseline for semantic segmentation of sparse outdoor point clouds.

Abstract (translated)

在自动驾驶数据集上对点云进行语义分割需要处理大量点在一个广阔的视野范围内,目前大多数为该任务设计的深度学习网络利用3D稀疏卷积来降低内存和计算负载。最佳方法进一步利用旋转激光雷达采样模式的特殊性质,以进一步提高性能,例如圆柱形立方体素数或距离图像(用于多个点云表示的特征融合)。与之相反,我们表明,可以在这些 specialized tools 之外建立良好的点为基础的骨架。这个骨架称为WaffleIron, heavily rely on generic MLPs 和密集2D卷积,使其易于实现,并只包含几个容易调整的参数。尽管其简单,我们在语义KITTI和nuScenes实验中表明,WaffleIron与专门为此自动驾驶数据集设计的最优方法竞争。因此,WaffleIron是一个强大的、易于实现的基准,用于稀疏室外点云的语义分割。



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