论文标题
多分辨率的图形神经网络，用于大规模点云进行分割
Multi-Resolution Graph Neural Network for Large-Scale Pointcloud Segmentation
论文作者
论文摘要
在本文中，我们提出了一个多分辨率深度学习的体系结构，以进行语义段密集的大规模点云。密集的PointCloud数据需要在语义分割之前进行计算昂贵的特征编码过程。以前的工作已使用不同的方法从原始的PointCloud大幅度下样本，因此可以使用常见的计算硬件。尽管这些方法可以在某种程度上减轻计算负担，但它们的处理能力仍然有限。我们提出MUGNET，这是一种记忆效率的端到端图神经网络框架，可在大规模的点云上执行语义分割。我们通过在预先形成的PointCloud图上利用图形神经网络来减少计算需求，并使用融合在不同分辨率的双向网络中保留分割的精度。我们的框架已在基准数据集上进行了验证，包括Stanford大规模3D室内空间数据集（S3DIS）和虚拟Kitti数据集。我们证明，我们的框架可以在单个11 GB GPU上立即处理多达45次房间扫描，同时仍超过其他基于图的解决方案，用于在S3DIS上进行分割，并以88.5 \％（+3 \％）的整体准确性和69.8 \％（+7.7.7 \％）MIOU准确度。
In this paper, we propose a multi-resolution deep-learning architecture to semantically segment dense large-scale pointclouds. Dense pointcloud data require a computationally expensive feature encoding process before semantic segmentation. Previous work has used different approaches to drastically downsample from the original pointcloud so common computing hardware can be utilized. While these approaches can relieve the computation burden to some extent, they are still limited in their processing capability for multiple scans. We present MuGNet, a memory-efficient, end-to-end graph neural network framework to perform semantic segmentation on large-scale pointclouds. We reduce the computation demand by utilizing a graph neural network on the preformed pointcloud graphs and retain the precision of the segmentation with a bidirectional network that fuses feature embedding at different resolutions. Our framework has been validated on benchmark datasets including Stanford Large-Scale 3D Indoor Spaces Dataset(S3DIS) and Virtual KITTI Dataset. We demonstrate that our framework can process up to 45 room scans at once on a single 11 GB GPU while still surpassing other graph-based solutions for segmentation on S3DIS with an 88.5\% (+3\%) overall accuracy and 69.8\% (+7.7\%) mIOU accuracy.
