Voxel-based 3D object classification has been thoroughly studied in recent years. Most previous methods convert the classic 2D convolution into a 3D form that will be further applied to objects with binary voxel representation for classification. However, the binary voxel representation is not very effective for 3D convolution in many cases. In this paper, we propose a hybrid cascade architecture for voxel-based 3D object classification. It consists of three stages composed of fully connected and convolutional layers, dealing with easy, moderate, and hard 3D models respectively. Both accuracy and speed can be balanced in our proposed method. By giving each voxel a signed distance value, an obvious gain regarding the accuracy can be observed. Besides, the mean inference time can be speeded up hugely compared with the state-of-the-art point cloud and voxel based methods.

翻译：近年来，基于体素的3D物体分类研究已得到深入探索。以往多数方法将经典二维卷积转化为三维形式，并进一步应用于具有二值体素表示的物体分类任务中。然而在许多场景下，二值体素表示对三维卷积的效果并不理想。本文提出一种面向体素化三维物体分类的混合级联架构，该架构由全连接层与卷积层构成的三级处理阶段组成，分别处理简单、中等及困难三类三维模型。所提方法能够实现精度与速度的平衡。通过为每个体素赋予符号距离值，可观察到准确率获得显著提升。此外，与当前最优的基于点云及体素的方法相比，本方法的平均推理时间实现了大幅加速。