Although predecessors have made great contributions to the semantic segmentation of 3D indoor scenes,there still exist some challenges in the debris recognition of terrain data.Compared with hundreds of thousands of i...Although predecessors have made great contributions to the semantic segmentation of 3D indoor scenes,there still exist some challenges in the debris recognition of terrain data.Compared with hundreds of thousands of indoor point clouds,the amount of terrain point cloud is up to millions.Apart from that,terrain point cloud data obtained from remote sensing is measured in meters,but the indoor scene is measured in centimeters.In this case,the terrain debris obtained from remote sensing mapping only have dozens of points,which means that sufficient training information cannot be obtained only through the convolution of points.In this paper,we build multi-attribute descriptors containing geometric information and color information to better describe the information in low-precision terrain debris.Therefore,our process is aimed at the multi-attribute descriptors of each point rather than the point.On this basis,an unsupervised classification algorithm is proposed to divide the point cloud into several terrain areas,and regard each area as a graph vertex named super point to form the graph structure,thus effectively reducing the number of the terrain point cloud from millions to hundreds.Then we proposed a graph convolution network by employing PointNet for graph embedding and recurrent gated graph convolutional network for classification.Our experiments show that the terrain point cloud can reduce the amount of data from millions to hundreds through the super point graph based on multi-attribute descriptor and our accuracy reached 91.74%and the IoU reached 94.08%,both of which were significantly better than the current methods such as SEGCloud(Acc:88.63%,IoU:89.29%)and PointCNN(Acc:86.35,IoU:87.26).展开更多
A new wear-graphy technology was developed, which can simultaneously identify the shape and composition of wear debris, for both metals and non-metals. The fundamental principles of the wear-graphy system and its wear...A new wear-graphy technology was developed, which can simultaneously identify the shape and composition of wear debris, for both metals and non-metals. The fundamental principles of the wear-graphy system and its wear-gram system are discussed here. A method was developed to distribute wear debris on a slide uniformly to reduce overlapping of wear debris while smearing. The composition identification ana-lyzes the wear debris using the scanning electron microscope (SEM) energy spectrum, infrared-thermal im-aging and X-ray imaging technology. A wear debris analysis system based on database techniques is demon-strated, and a visible digitized wear-gram is acquired based on the information of wear debris with image collection and processing of the wear debris. The method gives the morphological characteristics of the wear debris, material composition identification of the wear debris, intelligent recognition of the wear debris, and storage and management of wear debris information.展开更多
基金This research was funded by grant from the Key Research and Development Program of Shaanxi Province(2018NY-127,2019ZDLNY07-02-01,2020NY-205)National Undergraduate Training Program for Innovation and entrepreneurship plan(S201910712240,X201910712080).
文摘Although predecessors have made great contributions to the semantic segmentation of 3D indoor scenes,there still exist some challenges in the debris recognition of terrain data.Compared with hundreds of thousands of indoor point clouds,the amount of terrain point cloud is up to millions.Apart from that,terrain point cloud data obtained from remote sensing is measured in meters,but the indoor scene is measured in centimeters.In this case,the terrain debris obtained from remote sensing mapping only have dozens of points,which means that sufficient training information cannot be obtained only through the convolution of points.In this paper,we build multi-attribute descriptors containing geometric information and color information to better describe the information in low-precision terrain debris.Therefore,our process is aimed at the multi-attribute descriptors of each point rather than the point.On this basis,an unsupervised classification algorithm is proposed to divide the point cloud into several terrain areas,and regard each area as a graph vertex named super point to form the graph structure,thus effectively reducing the number of the terrain point cloud from millions to hundreds.Then we proposed a graph convolution network by employing PointNet for graph embedding and recurrent gated graph convolutional network for classification.Our experiments show that the terrain point cloud can reduce the amount of data from millions to hundreds through the super point graph based on multi-attribute descriptor and our accuracy reached 91.74%and the IoU reached 94.08%,both of which were significantly better than the current methods such as SEGCloud(Acc:88.63%,IoU:89.29%)and PointCNN(Acc:86.35,IoU:87.26).
基金Supported by the National Natural Science Foundation of China (No. 5017069)
文摘A new wear-graphy technology was developed, which can simultaneously identify the shape and composition of wear debris, for both metals and non-metals. The fundamental principles of the wear-graphy system and its wear-gram system are discussed here. A method was developed to distribute wear debris on a slide uniformly to reduce overlapping of wear debris while smearing. The composition identification ana-lyzes the wear debris using the scanning electron microscope (SEM) energy spectrum, infrared-thermal im-aging and X-ray imaging technology. A wear debris analysis system based on database techniques is demon-strated, and a visible digitized wear-gram is acquired based on the information of wear debris with image collection and processing of the wear debris. The method gives the morphological characteristics of the wear debris, material composition identification of the wear debris, intelligent recognition of the wear debris, and storage and management of wear debris information.
