Underwater 3-D Scene Reconstruction Using Kinect v2 Based on Physical Models for Refraction and Time of Flight Correction

Anwer, Atif; Ali, Syed Saad Azhar; Khan, Amjad; Mériaudeau, Fabrice

doi:10.1109/access.2017.2733003

Cited by 44 publications

(21 citation statements)

References 23 publications

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“…Digumarti et al [97] used Intel RealSense to develop a cheap and compact solution that enabled handheld scanning of marine life for divers. They scanned the coral reef in the ocean at a depth of 20 m. Anwer et al [147] used Kinect v2 from above the water to measure objects in a water tank. They found out that their measurements were very noisy, so they additionally applied a median filter that could partly deal with the noise.…”

Section: Quantitative Analysis Of Current Technologiesmentioning

confidence: 99%

“…Scanners that are able to provide more frames per second are more suitable for scenes with faster dynamics. The reported refresh rates are 0.1 Hz (Risholm et al [136]), 0.1 Hz to 6 Hz, depending on lateral resolution (Palomer et al [48]), 0.2 Hz (Imaki et al [82]), 1 Hz (Digumarti et al [97] with Intel RealSense) and 10 Hz (Risholm et al [76,134] and Anwer et al [147] with Kinect v2). Typical underwater manipulation tasks require a refresh rate higher than a few hertz.…”

Section: Quantitative Analysis Of Current Technologiesmentioning

confidence: 99%

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State of the Art of Underwater Active Optical 3D Scanners

Castillón

Palomer

Forest

et al. 2019

Sensors

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Underwater inspection, maintenance and repair (IMR) operations are being increasingly robotized in order to reduce safety issues and costs. These robotic systems rely on vision sensors to perform fundamental tasks, such as navigation and object recognition and manipulation. Especially, active optical 3D scanners are commonly used due to the domain-specific challenges of underwater imaging. This paper presents an exhaustive survey on the state of the art of optical 3D underwater scanners. A literature review on light projection and light-sensing technologies is presented. Moreover, quantitative performance comparisons of underwater 3D scanners present in the literature and commercial products are carried out.

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Section: Quantitative Analysis Of Current Technologiesmentioning

confidence: 99%

Section: Quantitative Analysis Of Current Technologiesmentioning

confidence: 99%

State of the Art of Underwater Active Optical 3D Scanners

Castillón

Palomer

Forest

et al. 2019

Sensors

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show abstract

“…The normal vector is a fundamental attribute of point cloud. The accuracy of the normal vector directly affects the application effect of point cloud data in reverse engineering, and the rendering and processing of point cloud in many other areas, such as denoising, segmentation, data reduction, and surface reconstruction [11][12][13][14]. For surfaces with sharp features, the details of the surfaces are easily lost in point cloud processing, if the normal vector of the feature region, i.e.…”

Section: Pca-based Normal Vector Estimation and Outlier Correctionmentioning

confidence: 99%

Point Cloud Denoising with Principal Component Analysis and a Novel Bilateral Filter

Zhang¹,

Zhang²,

Yang³

et al. 2019

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This paper aims to remove the noises of different scales in point cloud data captured by 3D scanners, while preserving the sharp features (e.g. edges) of the model. For this purpose, the authors proposed a point cloud denoising method based on the principal component analysis (PCA) and a self-designed bilateral filter. First, the outliers in the point cloud were divided into isolated outliers and deviation outliers. The former was directly removed, while the latter was moved along the normal vector estimated by the PCA. Next, a bilateral filter was developed based on vertex brightness, vertex position and normal vector. During image processing, the grayscale of the current point was replaced with the weighted mean of the grayscales of its neighborhood points. The weight function is related to the distance and grayscale difference between the current point and neighborhood points. The effectiveness of our method was proved through experiments on actual point clouds. The results demonstrate that our bilateral filter can retain the sharp features of point cloud data, in addition to removing the small-scale noises.

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“…Kawasaki et al used a one-shot active technique with a diffractive optical element [12]. Anwer et al used a ToF camera and refraction undistortion technique to reconstruct an underwater scene [3].…”

Section: Related Workmentioning

confidence: 99%

Unified Underwater Structure-from-Motion

Ichimaru

Taguchi

Kawasaki

2019

2019 International Conference on 3D Vision (3DV)

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This paper shows that accurate underwater 3D shape reconstruction is possible using a single camera, observing a target through a refractive interface. We provide unified reconstruction techniques for a variety of scenarios such as single static camera and moving refractive interface, single moving camera and static refractive interface, and single moving camera and moving refractive interface. In our basic setup, we assume that the refractive interface is planar, and simultaneously estimate the unknown transformations of the planar interface and the camera, and the unknown target shape using bundle adjustment. We also extend it to relax the planarity assumption, which enables us to use waves of the refractive interface for the reconstruction task. Experiments with real data show the superiority of our method to existing methods.

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Underwater 3-D Scene Reconstruction Using Kinect v2 Based on Physical Models for Refraction and Time of Flight Correction

Cited by 44 publications

References 23 publications

State of the Art of Underwater Active Optical 3D Scanners

State of the Art of Underwater Active Optical 3D Scanners

Point Cloud Denoising with Principal Component Analysis and a Novel Bilateral Filter

Unified Underwater Structure-from-Motion

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