Active Planning of AUVs for 3D Reconstruction of Underwater Object using Imaging Sonar

Kim, Byeongjin; Kim, Jason; Lee, Meungsuk; Sung, Minsung; Yu, Son-Cheol

doi:10.1109/auv.2018.8729706

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…The imaging sonar approach for creating 3D point clouds has flaws, such as the frontal surface's unacceptable slope, sparse data, missing side and back information. To address these issues, Kim et al [174] proposed a multiple-view scanning approach to replace the single-view scanning method. They applied the spotlight expansion impact to obtain the 3D data of the underwater target.…”

Section: Dark Acoustic Shadowmentioning

confidence: 99%

Overview of Underwater 3D Reconstruction Technology Based on Optical Images

Wang

Shen

et al. 2023

JMSE

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At present, 3D reconstruction technology is being gradually applied to underwater scenes and has become a hot research direction that is vital to human ocean exploration and development. Due to the rapid development of computer vision in recent years, optical image 3D reconstruction has become the mainstream method. Therefore, this paper focuses on optical image 3D reconstruction methods in the underwater environment. However, due to the wide application of sonar in underwater 3D reconstruction, this paper also introduces and summarizes the underwater 3D reconstruction based on acoustic image and optical–acoustic image fusion methods. First, this paper uses the Citespace software to visually analyze the existing literature of underwater images and intuitively analyze the hotspots and key research directions in this field. Second, the particularity of underwater environments compared with conventional systems is introduced. Two scientific problems are emphasized by engineering problems encountered in optical image reconstruction: underwater image degradation and the calibration of underwater cameras. Then, in the main part of this paper, we focus on the underwater 3D reconstruction methods based on optical images, acoustic images and optical–acoustic image fusion, reviewing the literature and classifying the existing solutions. Finally, potential advancements in this field in the future are considered.

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Section: Dark Acoustic Shadowmentioning

confidence: 99%

Overview of Underwater 3D Reconstruction Technology Based on Optical Images

Wang

Shen

et al. 2023

JMSE

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“…Crosstalk noise occurs near an object when the periphery region is darker. Thus, in some studies [22], [36], crosstalk noise was removed by tracking the highlight and shadow of an underwater object from a frame where no crosstalk noise appears. However, this method presents two limitations.…”

Section: B Crosstalk Noisementioning

confidence: 99%

Crosstalk Removal in Forward Scan Sonar Image Using Deep Learning for Object Detection

et al. 2019

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This study proposes the detection and removal of crosstalk noise using a convolutional neural network in images of forward scan sonar. Because crosstalk noise occurs near an underwater object and distorts the shape of the object, underwater object detection is limited. The proposed method can detect crosstalk noise using the neural network and remove crosstalk noise based on the detection result. Thus, the proposed method can be applied to other sonar-image-based algorithms and enhance the reliability of those algorithms. We applied the proposed method to a three-dimensional point cloud generation and generated a more accurate point cloud. We verified the performance of the proposed method by performing multiple indoor and field experiments.

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“…15 Joe et al 16 have employed simulators for two different sonar sensors to create sonar fusion-based mapping algorithms. Kim et al 17 have simulated sonar images representing different object shapes to validate their algorithms for three-dimensional reconstruction. Sung et al 10 have utilized GAN models trained on pairs of synthetic images generated by simulators and real sonar images to further enhance the realism of the synthesized sonar images.…”

Section: Introductionmentioning

confidence: 99%

Cycle-GAN-based synthetic sonar image generation for improved underwater classification

Koo,

Youm,

Shin

2024

Ocean Sensing and Monitoring XVI

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Active Planning of AUVs for 3D Reconstruction of Underwater Object using Imaging Sonar

Cited by 9 publications

References 4 publications

Overview of Underwater 3D Reconstruction Technology Based on Optical Images

Overview of Underwater 3D Reconstruction Technology Based on Optical Images

Crosstalk Removal in Forward Scan Sonar Image Using Deep Learning for Object Detection

Cycle-GAN-based synthetic sonar image generation for improved underwater classification

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