基于竞争注意力融合的深度三维点云分类网络

为了提高三维点云深度网络分类模型对全局特征的提取与表达能力，增强模型对噪声干扰的鲁棒性，提出可迁移应用于不同分类网络的竞争性注意力融合模块，学习多层级特征的全局表征和中间特征内在相似度，对中间特征通道权值重分配. 在基准网络Pointnet++和PointASNL中嵌入所提模块并进行实验，结果显示：所提模块具有独立性和可迁移性，聚焦更利于三维点云形状分类的核心骨干特征. 与基准网络相比，所提模块在保持分类精度稳定不下降的情况下，模型对点云扰动噪声、离群点噪声和随机噪声的抗干扰能力增强，在随机噪声数分别为0、10、50、100、200的情况下，准确度分别达到93.2%、92.9%、85.7%、78.2%、63.5%. 与传统滤波方法相比，端到端的学习减少预处理步骤和人工干预过程，同时具有更优的抗噪性能.

Deep 3D point cloud classification network based on competitive attention fusion

A competitive attention fusion block that can be transferred to different classification networks was proposed, in order to improve the 3D point cloud deep network classification model’s ability to extract and express global features, and enhance the model’s robustness to noise interference. The global representation of multi-hierarchical features and the internal similarity of intermediate features were learned. The weights of the intermediate feature channels were re-allocated. The proposed block was embedded in the benchmark networks Pointnet++ and PointASNL for experiments. Results show that the proposed block is independent and transferable, focusing on the core and backbone features that are more conducive to 3D point cloud shape classification. Compared with the benchmark network, the proposed block enhances the model’s anti-interference ability to point cloud disturbance noise, outlier noise and random noise without decreasing the classification accuracy. The proposed method achieves the accuracy was 93.2%, 92.9%, 85.7%, 78.2%, 63.5% in the case of the number of random noises was 0, 10, 50, 100, 200, respectively. Compared with the traditional filtering method, end-to-end learning reduces the pre-processing steps and manual intervention process, and has better anti-noise performance.