基于卦限卷积神经网络的3D点云分析

基于深度学习的三维点云数据分析技术得到了越来越广泛的关注, 然而点云数据的不规则性使得高效提取点云中的局部结构信息仍然是一大研究难点. 本文提出了一种能够作用于局部空间邻域的卦限卷积神经网络(Octant convolutional neural network, Octant-CNN), 它由卦限卷积模块和下采样模块组成. 针对输入点云, 卦限卷积模块在每个点的近邻空间中定位8个卦限内的最近邻点, 接着通过多层卷积操作将8卦限中的几何特征抽象成语义特征, 并将低层几何特征与高层语义特征进行有效融合, 从而实现了利用卷积操作高效提取三维邻域内的局部结构信息; 下采样模块对原始点集进行分组及特征聚合, 从而提高特征的感受野范围, 并且降低网络的计算复杂度. Octant-CNN通过对卦限卷积模块和下采样模块的分层组合, 实现了对三维点云进行由底层到抽象、从局部到全局的特征表示. 实验结果表明, Octant-CNN在对象分类、部件分割、语义分割和目标检测四个场景中均取得了较好的性能.

Octant Convolutional Neural Network for 3D Point Cloud Analysis

The 3D point cloud data analysis based on deep learning has attracted increasing attention recently. However, it is still a great challenge to extract local structure information from point cloud efficiently due to its irregularity. In this paper, we propose a new network named octant convolutional neural network (Octant-CNN) which can handle local spatial neighborhoods. It consists of octant convolution module and sub-sampling module. For the input point cloud, the octant convolution module locates nearest points in eight octants of each point, and then transforms the geometric features into semantic features through a multi-layer convolution operation. The low-level geometric features are effectively fused with the high-level semantic features so that the local structure information can be efficiently extracted. The sub-sampling module groups the original point set and aggregates the features to expand the receptive field of features, and also reduce the computation overhead of the network. By stacking the octant convolution module and sub-sampling module, Octant-CNN obtains the feature representation of the 3D point cloud from low-level to abstract, and from local to global. Extensive experiments demonstrate that Octant-CNN achieves great performance in four 3D scene understanding tasks including object classification, part segmentation, semantic segmentation, and object detection.