Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators

Shinohara, Takayuki; Xiu, Haoyi; Matsuoka, M.

doi:10.1109/jstars.2021.3124610

Cited by 1 publication

(1 citation statement)

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“…In paper [37], the authors proposed a deep-learning based fusion framework to combine the complementary information from hyperspectral and full-waveform LiDAR data for tree species mapping. In paper [38], the authors tried to develop an end-to-end neural network model (Point2wave) for translating 3-D point clouds into their missing waveforms of full-waveform LiDAR data using the SR-GAN. Also, the experimental results showed that, Point2wave is able to translate the 3-D point clouds into desired waveform signals, and the translated waveform signals achieved nearly the same classification performance as the real waveforms.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Full-Waveform Echo Decomposition Based on Self-Attention Classification and U-Net Decomposition

Liu

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Different from conventional decomposition methods which utilize several steps to obtain the final result, a selfattention based neural network, Attention Full-waveform Decomposition Network (AFD-Net), is discussed in this paper for endto-end full-waveform LiDAR signal decomposition. In existing LiDAR waveform decomposition methods, complicate functional models are used to fit echo components. Thus, the echo decomposition problem can be translated into a function approximation task. Recent studies present great progress in estimating the parameters of fitting models, hence in the final decomposition results. However, the shape of received echo components are always irregular. None of the parametric functional models can fit the received echo components perfectly, which leads to unavoidable errors in the initial step of echo decomposition. In this paper, we propose an end-to-end net work AFD-Net to solve the echo decomposition problem without assuming any parametric functional models. AFD-Net is consisted with two modules, the classification module and the decomposition module. The former module is used to determine the number of echo components in a received waveform. Then the decomposition module is used to output the echo components. By experiments, we have a classification accuracy 96% using the first module. The average R 2 coefficient for the decomposed echo components is 0.9799. In addition, there are no public datasets for the waveform decomposition task available. Thus, another contribution of our work is to develop a tool to generate synthetic full-waveform LiDAR signals, which can help researchers to construct their own dataset for related works. All of our source codes are available in the following site: https://github.com/ZedFm/AFD-Net

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Section: Introductionmentioning

confidence: 99%

End-to-End Full-Waveform Echo Decomposition Based on Self-Attention Classification and U-Net Decomposition

Liu

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Point2Wave: 3-D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network With Dual Discriminators

Cited by 1 publication

References 58 publications

End-to-End Full-Waveform Echo Decomposition Based on Self-Attention Classification and U-Net Decomposition

End-to-End Full-Waveform Echo Decomposition Based on Self-Attention Classification and U-Net Decomposition

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