3D Point Cloud Restoration via Deep                         Learning: A Comprehensive                 Survey

Liu, Caixia; Wei, Mingqiang; Guo, Yanwen

doi:10.3724/sp.j.1089.2021.18817

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Compared with other deep learning structures, convolutional neural networks require fewer parameters, but can achieve better results in image and speech recognition tasks. The standard CNN consists of a deep architecture of a series of different layers, mainly including an input layer, a series of convolution layers, pooling layers, and fully connected layers [15] . A 6-layer CNN model is proposed to control the parameters in a small range.…”

Section: Convolutional Neural Network Voice Recognition For Transmiss...mentioning

confidence: 99%

Research on convolutional neural network method for transmission line abnormal sound identification

Cai

Sun

Liu

et al. 2022

Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022)

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At present, most of the hidden troubles in transmission lines are detected by image and video technology, however, there are too many monitoring targets, due to the influence of weather changes and lighting, and other factors, it is difficult to accurately monitor the hidden troubles of transmission lines only by video images, this paper presents a convolutional neural network method to identify the abnormal sound in transmission lines. The audio data collected by the audio-visual acquisition device is processed by the convolutional neural network to identify the potential audio data, filter the nonpotential audio data, and improve the speed of potential danger analysis. Through the training and testing of sound multilabel classification and detection algorithm, the validity of the method is verified.

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Section: Convolutional Neural Network Voice Recognition For Transmiss...mentioning

confidence: 99%

Research on convolutional neural network method for transmission line abnormal sound identification

Cai

Sun

Liu

et al. 2022

Fifth International Conference on Mechatronics and Computer Technology Engineering (MCTE 2022)

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“…Design and implementation of remote sensing monitoring system for resources and environment based on lidar technology [11]: a new classification method of remote sensing monitoring data is proposed by using airborne lidar and hyperspectral technology [12]. is paper discusses the 3D point cloud repair technology based on deep learning from five aspects [13]. According to the situation of WTO, the actual situation of mining heritage is analyzed to promote the two-way win-win of mining heritage protection and tourism development [14].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Reconstruction and Protection of Mining Heritage Based on Lidar Remote Sensing and Deep Learning

Shang

Wang

2022

Mobile Information Systems

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In the past, the traditional small-scale 3D reconstruction technology developed maturely and mainly relied on manual handheld devices for data acquisition. Due to multiple factors, inaccuracy, and other problems, data will be omitted, and sometimes staff will face risks directly. Nowadays, with the development of computer science and technology, three-dimensional reconstruction technology has been innovated continuously, and it has also made a breakthrough in the application of large-scale reconstruction of dangerous areas. In this paper, the expensive sensors in the past are abandoned, and the cheaper and more efficient lidar is used instead. The laser point cloud and image are combined to describe the mining heritage scene in depth, and the academic achievements are transformed into productivity. The results show that (1) comparing the performance of the algorithm, the overall error of the proposed method is 5.86, and the average time consumption is about 10.23 ms. This filtering algorithm can restore the landscape of mining heritage to a great extent and optimize the problems of noise and outliers. (2) Our designed model has a very low loss value, and the point cloud accuracy is as high as 92.9%. Compared with other model methods, the model has excellent performance. (3) After the completion of the model, the overall satisfaction effect is above 70%, which can well restore the mining heritage style. Finally, the experimental effect of 3D reconstruction is good, which is more conducive to the research of reconstruction protection. There are still some work details and performance problems that need to be optimized and solved.

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A Point Cloud Completion Method for Industrial Component Based on Multiple Encoders

Wang,

Chen,

Huang

et al. 2023

2023 China Automation Congress (CAC)

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3D Point Cloud Restoration via Deep Learning: A Comprehensive Survey

Cited by 3 publications

References 30 publications

Research on convolutional neural network method for transmission line abnormal sound identification

Research on convolutional neural network method for transmission line abnormal sound identification

Three-Dimensional Reconstruction and Protection of Mining Heritage Based on Lidar Remote Sensing and Deep Learning

A Point Cloud Completion Method for Industrial Component Based on Multiple Encoders

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