An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression
Authors: Luo, G., He, B., Xiong, Y., Wang, L., Wang, H., Zhu, Z. and Shi, X.
Journal: Sensors
Volume: 23
Issue: 4
ISSN: 1424-8220
DOI: 10.3390/s23042250
Abstract:Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks.
https://eprints.bournemouth.ac.uk/40405/
Source: Scopus
An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression.
Authors: Luo, G., He, B., Xiong, Y., Wang, L., Wang, H., Zhu, Z. and Shi, X.
Journal: Sensors (Basel)
Volume: 23
Issue: 4
eISSN: 1424-8220
DOI: 10.3390/s23042250
Abstract:Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks.
https://eprints.bournemouth.ac.uk/40405/
Source: PubMed
An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression
Authors: Luo, G., He, B., Xiong, Y., Wang, L., Wang, H., Zhu, Z. and Shi, X.
Journal: SENSORS
Volume: 23
Issue: 4
eISSN: 1424-8220
DOI: 10.3390/s23042250
https://eprints.bournemouth.ac.uk/40405/
Source: Web of Science (Lite)
An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression.
Authors: Luo, G., He, B., Xiong, Y., Wang, L., Wang, H., Zhu, Z. and Shi, X.
Journal: Sensors (Basel, Switzerland)
Volume: 23
Issue: 4
Pages: 2250
eISSN: 1424-8220
ISSN: 1424-8220
DOI: 10.3390/s23042250
Abstract:Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks.
https://eprints.bournemouth.ac.uk/40405/
Source: Europe PubMed Central
An optimized convolutional neural network for the 3D point-cloud compression
Authors: Luo, G., He, B., Xiong, Y., Wang, L., Wang, H., Zhu, Z. and Shi, X.
Journal: Sensors
Volume: 23
Issue: 4
ISSN: 1424-8220
Abstract:Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks.
https://eprints.bournemouth.ac.uk/40405/
Source: BURO EPrints