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

A Christoper Tamilmathi,P L Chithra

doi:10.3389/frobt.2021.606770

Abstract

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.

Highlights

A LiDAR is an active optical technique that creates the high-density 3D point cloud image of sampled Earth’s surface by transmitting the signal pulses toward the target image in the Earth, detects and analyses the signal from the target by receiver sensor in the LiDAR
This is the first deep learning-based model implemented on 3D airborne LiDAR pcd image compression
The experimental results show that the proposed model compresses every pcd image into 16 Number of Neurons of data and decompresses the image with approximately 160 dB of Peak Signal-to-noize Ratio (PSNR) value, 174.46 s execution time with 0.6 execution speed per instruction and proved that it outperforms the other existing algorithms regarding space and time complexity

Summary

INTRODUCTION

A LiDAR is an active optical technique that creates the high-density 3D point cloud image of sampled Earth’s surface by transmitting the signal pulses toward the target image in the Earth, detects and analyses the signal from the target by receiver sensor in the LiDAR. The deep learned quantization model has taken the transformed signal data as the input and produces the latent vector as a compressed form of bitstream data This model has been implemented and tested on three different dense airborne LiDAR pcd datasets and compared with the existing algorithms. Experimental results show that the proposed DLQCPCD algorithm compresses every pcd image into constant 16-bits of data and the quality of the reconstructed image averagely increased by 67.01% on average compared to the other function combination This is the first deep learning-based model implemented on 3D airborne LiDAR pcd image compression. The bias value is linked with all the neurons in each layer This proposed quantization architecture has been deep learned by applying the Mean Squared Error (MSE) loss function to calculate the distortion between actual and targeted output pcd image is denoted in Eq 6.

EXPERIMENTAL RESULTS

CONCLUSION

DATA AVAILABILITY STATEMENT