基于动态图卷积和PointNet的三维局部特征描述符
Three-Dimensional Local Feature Descriptor based on Dynamic Graph Convolution and PointNet
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摘要: 三维局部特征描述是三维计算机视觉中的基础任务. 现有的方法要么依赖于对噪声敏感的手工特征, 要么依赖于不具有旋转不变性的神经网络结构. 本文提出一种基于动态图卷积和PointNet的三维局部特征描述符生成网络(Dynamic Graph Convolution and PointNet based Network, DGCPNet)来提取具有旋转不变性和强泛化性的局部特征描述符. 首先, 将与局部参考框架(Local Reference Frame, LRF)对齐后的局部点云作为网络的输入, 分别通过动态图卷积模型和PointNet模型提取输入点云中的局部几何特征和点特征, 解决单一PointNet模型无法学习输入点集中点与点之间关系的问题. 然后, 为了进一步提高网络的学习能力, 本文提出一个由点自注意力模块(Point Self-Attention, PSA)和局部空间注意力模块(Local Spatial-Attention, LSA)组成的双重注意力机制层, 用于更好地融合两个模型提取到的特征, 来获取最终的描述符特征. 在室内和室外数据集上的大量实验表明, DGCPNet在描述性、鲁棒性和泛化性方面均优于现有的方法.Abstract: Three-dimensional (3-D) local feature description is fundamental task in 3-D computer vision. Existing methods either rely on noise-sensitive handcrafted features, or depend on rotation-variant neural network structures. This paper proposes rotation-invariant and general local feature descriptor named DGCPNet by combining Dynamic Graph Convolution and PointNet. First, a local patch is aligned with a Local Reference Frame (LRF), and used as an input of our network. Then, local geometric features and point features are extracted by a dynamic graph convolution model and a PointNet model, respectively. This resolves the issue that a single PointNet model is unable to learn the relationships between points in the input point set. Finally, to further improve the learning ability of the network, a dual-attention mechanism layer, containing a Point Self-Attention (PSA) module and a Local Spatial-Attention (LSA) module, is proposed to integrate the local geometric features and the point features, obtaining the final descriptor features. Extensive experiments on the indoor and outdoor datasets demonstrate that DGCPNet outperforms existing methods in terms of descriptiveness, robustness, and generalization.