Three-dimensional models are widely used in the fields of multimedia, computer graphics, virtual reality, entertainment, design, and manufacturing because of the rich information that preserves the surface, color and texture of real objects. Therefore, effective 3D object classification technology has become an urgent need. Previous methods usually directly convert classic 2D convolution into 3D form and apply it to objects with binary voxel representation, which may lose internal information that is essential for recognition. In this paper, we propose a novel voxel-based three-view hybrid parallel network for 3D shape classification. This method first obtains the depth projection views of the three-dimensional model from the front view, the top view and the side view, so as to preserve the spatial information of the three-dimensional model to the greatest extent, and output its predicted probability value for the category of the three-dimensional model, and then combining the three-view parallel network with voxel sub-network performs weight fusion, and then uses Softmax for classification. We conducted a series of experiments to verify the design of the network and achieved competitive performance in the 3D object classification tasks of ModelNet10 and ModelNet40.

三维模型由于保留了真实物体的表面、颜色和纹理的丰富信息，被广泛应用于多媒体、计算机图形学、虚拟现实、娱乐、设计和制造等领域。因此，有效的 3D 对象分类技术已成为迫切需要。以前的方法通常将经典的 2D 卷积直接转换为 3D 形式并将其应用于具有二进制体素表示的对象，这可能会丢失识别所必需的内部信息。在本文中，我们提出了一种新的基于体素的三视图混合并行网络，用于 3D 形状分类。该方法首先从正视图、俯视图和侧视图获得三维模型的深度投影视图，从而最大程度地保留三维模型的空间信息，并输出其对三维模型所属类别的预测概率值，然后结合三视图并行网络和体素子网络进行加权融合，然后使用 Softmax 进行分类。我们进行了一系列实验来验证网络的设计，并在 ModelNet10 和 ModelNet40 的 3D 对象分类任务中取得了有竞争力的性能。