# VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification
## 2 Related Works  
Researchers employ many methods in 3D object recognition, i.e., machine learning-based methods, graph-based methods, and feature-based methods. Feature-based methods are usually employed when using point cloud data for classification tasks. Generally, there were two kinds of methods mainly used for feature-based object recognition: the global feature-based method and the local feature-based method. When adopting global feature-based methods, a segmentation step is required before classification. [PERSON] et al. [12] developed a global descriptor which consisted of point pair features. By grouping similar point pairs, a voting scheme could work on the local space for the classification job. Their methods performed well under a noise situation. [PERSON] et al. developed the global Fourier histogram (GFH) descriptor [13] that used cylindrical angular coordinates. They defined the cylindrical support region in [13] for calculating the GFH by counting the points in each region. With the help of a global reference frame [14] and Fourier transform, GFH also became rotation-invariant. Although global features performed well in many classification tasks, they failed to distinguish objects with similar shapes [13; 15]. Thus, researchers introduced some local features to address this and other problems with global features. [PERSON] et al. proposed Tri-Spin-Image (TriSI) [16] and overcame the weak point of the traditional SI descriptor. Employing principal component analysis and a local reference frame, TriSI was robust to noise and mesh resolution. [PERSON] et al. introduced a descriptor named local feature statistics histogram (LFSH) [17], which exploited histograms by counting local features around the chosen points using one-dimensional (1D) arrays. LFSH worked faster than other local feature descriptors, e.g., SI, yet employing 1D histograms led to some information loss. Numerous deep learning networks were employed for point cloud object classification works in recent years. Although some cloud architectures consume raw point cloud data directly [18; 19], many deep learning networks still require various preprocessing of point clouds. The Multiview 3D network proposed by [PERSON] et al. [20] converted 3D point clouds into 3D bounding boxes for multiview feature fusion. By combining these multiview features, their network enabled fast classification of objects. [PERSON] et al. proposed Kd-network [21] which did not require rasterization of point clouds before inputting into the network. Exploiting the indexing structure called Kd-tree, Kd-network saved the memory usage and reduced time consumption in the training and testing process. [PERSON] et al. introduced spherical convolutional neural network [22], which converted 3D models into spherical representations and took these spherical representations as the inputs of the network. The spherical representations reduced the input size and made the model become rotation-invariant at the same time. As researchers developed voxel representation for point clouds, they employed convolutional neural networks (CNNs) in 3D object classification tasks. [PERSON] et al. developed VoxelNet [23] that converted a point cloud into even voxels to encode features. The application of VoxelNet made it possible to employ a region proposal network for object classification in a point cloud. [PERSON] et al. proposed field-probing neural networks (FPNN) [24] that employed field-probing filters to extract features from voxels. The field-probing filters were adaptively distributed in 3D space, and their shape was changed to perceive 3D space more effectively. Compared to traditional 3D CNNs, FPNN performed well in 3D object classification tasks with low time consumption. [PERSON] et al. introduced NormalNet [25], which combined normal vectors of object surfaces and voxels of the 3D object for classification, because normal vectors of object surfaces had stronger capability in classification than voxels.