# GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification
## 2 Related Work
An orientation-coding element is formed to represent eight essential orientations, and a multi-scale description is achieved by accumulating the multiple orientation coding elements. The PointSIFT network enhances presentation capacity by blending PointSIFT modules into several PointNet-based structures. PointCNN [29] first applies \(\chi\)-transformation to resolve the obstacle of unordered andirregular structure in the point cloud, then uses the convolution approach for point cloud input. * Graph-based methods: Graph Signal Processing (GSP) can handle unstructured or unordered data. GSP for preparing a 3D point cloud has been a popular field at current times, with many applications such as compression, painting, and data visualization [30; 31; 32; 33]. GSP becomes essential because most data obtained can convert into a widespread graph. CNN introduces an effective architecture to exploit important patterns in regular structural data such as 2D images. Some approaches attempt to extend the concept of CNNs into widespread graphs, which are not straight because of the abnormal structure of these graphs. [PERSON] et al. [34] define a recursive kernel of Chebyshev polynomials to introduce a quick localized convolution process, making it quickly learn while keeping sufficient complexity of deep learning. [PERSON] et al. [35] apply a graph convolution described in the graph spectral field. Graph-based methods implement a graph with an individual vertex for every point and edges within nearby points instead of converting point cloud toward voxels. A Graph-CNN structure in [36] uses the local architecture of the 3D point cloud data encoded in graphs to analyze 3D point cloud data. Graph-CNN requires neighbor data of every point at the best level for building a solid graph. In contrast, ref. [16] dismisses the uselessness by extracting a higher compact graph and making GSP on the Reeb graph. In this work, the methods selected for comparison are based on data representation, including network structure. Those methods were fully representative of their subgroups. Table 1 summarizes the principal methods for the point cloud classification based on input data format, dataset type, network architecture, and model type. A total of fifteen methods in the four main approaches, except Multimodal Information Fusion Network (MIFN) in reference [15] and GSV-NET, use a single model, leading to low performance in the point cloud classification. MIFN fuses three different models: the point cloud model, view model, and PANORAMA-view model, to obtain more-accurate classification results than those fifteen methods. However, there are two main drawbacks in MIFN: missing real-world datasets, and both the view model and PANORAMA-view model depend on the camera settings. Camera settings make MIFN hard to apply in real-life applications due to missing-view problems. Besides, 4 among 17 methods have testing results with real-world datasets, while the others use computer datasets. Computer data are different from the real-life data taken from the LiDAR sensor. Ignoring real-world data is a big drawback because real-world data ensure that the method works correctly with practical applications such as real-life self-driving systems. GSV-NET performs best among 17 methods in the table due to some reasons. GSV-NET employs multi-modal to boost classification performance. Our framework fills the gap in the multi-modal approach. Unlike the first multi-modal, MIFN, GSV-NET effectively fuses model information and reduces the number of multi-modals. Specifically, GSV-NET has two models: point cloud model, and view model, compared with three models in MIFN: point cloud model, view model, and the PANORAMA-view model. In addition, GSV-NET handles two main drawbacks of MIFN: missing real-world datasets and camera-settings problems. In our proposed method, we first created three 2D point clouds from the original 3D point cloud based on the point locations and then convert them into three images, respectively. Hence, GSV-NET does not depend on camera settings.