Point cloud datasets often suffer from inadequate sample sizes in comparison to image datasets, making data augmentation challenging. While traditional methods, like rigid transformations and scaling, have limited potential in increasing dataset diversity due to their constraints on altering individual sample shapes, we introduce the Biharmonic Augmentation (BA) method. BA is a novel and efficient data augmentation technique that diversifies point cloud data by imposing smooth non-rigid deformations on existing 3D structures. This approach calculates biharmonic coordinates for the deformation function and learns diverse deformation prototypes. Utilizing a CoefNet, our method predicts coefficients to amalgamate these prototypes, ensuring comprehensive deformation. Moreover, we present AdvTune, an advanced online augmentation system that integrates adversarial training. This system synergistically refines the CoefNet and the classification network, facilitating the automated creation of adaptive shape deformations contingent on the learner status. Comprehensive experimental analysis validates the superiority of Biharmonic Augmentation, showcasing notable performance improvements over prevailing point cloud augmentation techniques across varied network designs.

翻译：点云数据集相较于图像数据集常面临样本量不足的问题，这使得数据增强颇具挑战性。虽然传统方法（如刚性变换与缩放）因限制单样本形状调整而难以有效提升数据集多样性，我们提出了双调和增强（Biharmonic Augmentation, BA）方法。BA是一种新颖高效的数据增强技术，通过对现有三维结构施加平滑的非刚性形变来丰富点云数据。该方法计算形变函数的双调和坐标并学习多样化形变原型，并利用CoefNet预测系数以整合这些原型，确保形变的全面性。此外，我们提出了AdvTune——一种集成对抗训练的高级在线增强系统。该系统协同优化CoefNet与分类网络，根据学习器状态自动生成自适应形状形变。综合实验分析验证了双调和增强的优越性，表明其在多种网络架构下均能显著超越现有主流点云增强技术。