Point cloud data classification method based on deep learning

Abstract

The invention discloses a point cloud data classification method based on deep learning. The method provides a multi-scale point cloud classification network, and comprises the steps of firstly, providing a multi-scale local area division algorithm on the basis of completeness, adaptivity, overlap and multi-scale characteristic requirements of the local area division, and obtaining a multi-scale local area by taking the point cloud and the characteristics of different levels as input; and then constructing the multi-scale point cloud classification network comprising a single-scale feature extraction module, a low-level feature aggregation module, a multi-scale feature fusion module and the like. The network fully simulates the action principle of the convolutional neural network, and hasthe basic characteristics that the local receptive field becomes larger and larger and the feature abstraction degree becomes higher and higher along with the increase of the network scale and depth.The method of the invention respectively obtains the 94.71% and 91.73% classification accuracies at the standard public data set ModelNet 10 and ModelNet 40, is in a leading or equivalent level in thesimilar work, and the feasibility and effectiveness of the method are verified.

Description

technical field [0001] The present invention relates to the technical fields of computer graphics, computer vision and intelligent recognition, in particular to a method for classifying point cloud data based on deep learning. Background technique [0002] With the wide application of 3D sensors such as lidar and RGBD cameras in the field of robots and unmanned driving, the acquisition of point cloud data is becoming more and more convenient and fast, and its position in 3D data is becoming more and more important. However, in order to make good use of point cloud data, how to quickly and efficiently identify point cloud data is a basic problem that needs to be solved urgently. [0003] Point cloud data is a form of representation of three-dimensional data. It is a collection of points scanned by various devices that record the shape of the outer surface of an object in the form of points. Usually, each point contains X, Y, and Z three-dimensional coordinate information. Wi...

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Problems solved by technology

Wu et al. (Wu Z, Song S, Khosla A, et al. 3D shapenets: A deep representation for volumetric shapes[C] / / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Washington DC: IEEE Computer Society Press. 2015: 1912-1920) first proposed the voxel-based 3D deep belief network 3DShapenets in 2015, and achieved 83.54% and 77.32% classification accuracy on the ModelNet10 and ModelNet40 standard data sets respectively, 3-5 points higher than traditional methods Percentage points. However, the nature of the voxel data itself makes the three-dimensional convolution calculation very redundant, and the performance is largely limited by the voxel resolution and the computational cost of exponential growth.

Due to the above shortcomings of three-dimensional convolution, Su et al. (Su H, Maji S, Kalogerakis E, et al. Multi-view convolutional neural networks for 3d shape recognition[C] / / Proceedings of the IEEE international conference on computervision.Washington D C:IEEE The MVCNN proposed by Computer Society Press, 2015:945-953) takes a set of two-dimensional views as input, constructs the initial features of the two-dimensional views through CNN, and fuses the features of each two-dimensional views through the view pooling layer to obtain three-dimensional data The characteristics of the classification are completed. The classification accuracy of this method on ModelNet40 is 89.9%, which is higher than the voxel-based deep learning classification method proposed in the same period. However, due to the existence of point cloud data (1) the point cloud is disordered; (2) The point cloud is sparse; (3) The amount of point cloud information is limited

Extending the above methods directly to point cloud data is not ideal

[0006] Qi et al. (C.R.Qi, H.Su, K.Mo, and L.J. Guibas. Pointnet: deep learning on pointsets for 3d classification and segmentation[C] / / Proceedings of the IEEEConference on Computer Vision and Pattern Recognition, pages 652–660, 2017.) The PointNet network proposed for the characteristics of point cloud data first applies deep learning to point cloud classification tasks. PointNet uses T-Net to achieve effective alignment of data and features, and uses Maxpooling symmetric function to extract order-independent global features. ModelNet40 A classification accuracy of 89.20% has been achieved on the above. However, PointNet cannot extract the local features of point cloud data, and a series of work has been designed on how to effectively extract the local features of point cloud data.

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