A fast reconstruction method of the dense point-cloud model for cultural heritage artifacts based on compressed sensing and sparse auto-encoder

Chen, Xiaoxue; Zhou, Mingquan; Zou, Li; Li, Fan; Hu, Jiabei; Zhang, Haibo

doi:10.1007/s11082-019-2038-y

Cited by 9 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Voxelized and scaled, non-overlapped 3D cube structure point cloud fed into the stacked convolutional network to improve the latent feature characteristics of the pcd image ( Bello et al, 2020 ). Another type of sparse autoencoder and compressed sensing method improves the speed of the reconstruction process ( Chen et al, 2019 ). The quality of the reconstruction point cloud image has been improved by the folded neural network with a tuned weight model ( Wang et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Deep Learned Quantization-Based Codec for 3D Airborne LiDAR Point Cloud Images

Tamilmathi

Chithra

2021

Front. Robot. AI

View full text Add to dashboard Cite

This paper introduces a novel deep learned quantization-based coding for 3D Airborne LiDAR (Light detection and ranging) point cloud (pcd) image (DLQCPCD). The raw pcd signals are sampled and transformed by applying the Nyquist signal sampling and Min-max signal transformation techniques, respectively for improving the efficiency of the training process. Then, the transformed signals are feed into the deep learned quantization module for compressing the data. To the best of our knowledge, this proposed DLQCPCD is the first deep learning-based model for 3D airborne LiDAR pcd compression. The functions of Mean Squared Error and Stochastic Gradient Descent optimization function enhance the quality of the decompressed image by 67.01 percent on average, compared to other functions. The model’s efficiency has been validated with established well-known compression techniques such as the 7-Zip, WinRAR, and tensor tucker decomposition algorithm on the three inconsistent airborne datasets. The experimental results show that the proposed model compresses every pcd image into constant 16 Number of Neurons of data and decompresses the image with approximately 160 dB of PSNR value, 174.46 s execution time with 0.6 s execution speed per instruction, and proved that it outperforms the other existing algorithms regarding space and time.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Learned Quantization-Based Codec for 3D Airborne LiDAR Point Cloud Images

Tamilmathi

Chithra

2021

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

“…At present, the state has paid attention to the protection of immovable cultural relics and formulated relevant laws and regulations and hopes to reduce the damage to cultural relics. Although it has achieved remarkable results [9], the impact of natural conditions is still severe.…”

Section: Introductionmentioning

confidence: 99%

Immovable Cultural Relics Disease Prediction Based on Relevance Vector Machine

Liu

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The preventive cultural relics protection is one of the most concerned contents in archaeology, which includes environmental monitoring and accurate prediction of cultural relics diseases. In view of the deficiency of the analysis of cultural relics data and the prediction of cultural relics diseases, a prediction model of immovable cultural relics diseases based on relevance vector machine (RVM) is proposed. The key factors affecting the disease of immovable cultural relics are found out by the principal component analysis method, and the dimension reduction of data is realized; then, the RVM model under the framework of Bayesian theory is constructed, and the super parameters are estimated by the maximum edge likelihood method; finally, the prediction accuracy of the model is compared with the traditional diseases prediction methods. The experiment results demonstrate that the proposed RVM-based immovable cultural relics disease prediction approach not only has the advantages of more sparse model but also has better prediction accuracy than the traditional radial basis function neural network-based and support vector machine-based methods.

show abstract