Abstract
Deep learning is robust to the perturbation of a point cloud, which is an important data form in the Internet of Things. However, it cannot effectively capture the local information of the point cloud and recognize the fine-grained features of an object. Different levels of features in the deep learning network are integrated to obtain local information, but this strategy increases network complexity. This paper proposes an effective point cloud encoding method that facilitates the deep learning network to utilize the local information. An axis-aligned cube is used to search for a local region that represents the local information. All of the points in the local region are available to construct the feature representation of each point. These feature representations are then input to a deep learning network. Two well-known datasets, ModelNet40 shape classification benchmark and Stanford 3D Indoor Semantics Dataset, are used to test the performance of the proposed method. Compared with other methods with complicated structures, the proposed method with only a simple deep learning network, can achieve a higher accuracy in 3D object classification and semantic segmentation.
Highlights
Big data from different sensors are the basis for the Internet of Things (IoT) to play its own advantages
Point cloud is an important 3D data format that is widely used in 3D semantic segmentation [1,2] and 3D object detection [3,4]
This paper proposes an effective method to encode each point into a feature representation with local information
Summary
Big data from different sensors are the basis for the Internet of Things (IoT) to play its own advantages. To utilize the deep learning method, some researchers converted raw point cloud data to regular formats such as 3D voxel grids [9] and multi-view renderings [10] This conversion increases the network complexity, and loses some useful information of the data. Li et al [15] proposed a SO-Net structure to utilize the spatial distribution of the point cloud These methods can make use of the local information, the models become very complicated compared with PointNet. This paper proposes an effective method to encode each point into a feature representation with local information. This paper proposes an effective method to encode each point into a feature representation with local information This method facilitates a deep learning network to utilize the local information.
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