论文标题
学习在2D图像空间中分段3D点云
Learning to Segment 3D Point Clouds in 2D Image Space
论文作者
论文摘要
与文献相比，定制的卷积操作员捕获了3D点云中的局部模式，在本文中，我们研究了如何有效，有效地将这种点云投射到2D图像空间中的问题，以便将传统的2D卷积神经网络（CNN）（例如U-NET）（例如U-NET）（例如U-NET）应用于分裂。为此，我们是通过图形绘图的动机，并将其作为整数编程问题进行重新将其重新制定，以了解每个单个点云的拓扑保存图形映射。为了加速实践中的计算，我们进一步提出了一种新型的分层近似算法。借助点云的Delaunay三角剖分，用于分割的多尺度U-NET，我们设法分别证明了Shapenet和Partnet上最新的性能，并在文献上进行了重大改进。代码可在https://github.com/zhang-vislab上找到。
In contrast to the literature where local patterns in 3D point clouds are captured by customized convolutional operators, in this paper we study the problem of how to effectively and efficiently project such point clouds into a 2D image space so that traditional 2D convolutional neural networks (CNNs) such as U-Net can be applied for segmentation. To this end, we are motivated by graph drawing and reformulate it as an integer programming problem to learn the topology-preserving graph-to-grid mapping for each individual point cloud. To accelerate the computation in practice, we further propose a novel hierarchical approximate algorithm. With the help of the Delaunay triangulation for graph construction from point clouds and a multi-scale U-Net for segmentation, we manage to demonstrate the state-of-the-art performance on ShapeNet and PartNet, respectively, with significant improvement over the literature. Code is available at https://github.com/Zhang-VISLab.
