Robust alternating optimisation for extrinsic calibration of RGB‐D cameras

Jang, Jae Won; Kwon, Young Chan; Hwang, Wonjun; Choi, Ouk

doi:10.1049/el.2019.1095

Cited by 2 publications

(2 citation statements)

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“…We extrinsically calibrated the two cameras using the methods in [70], [71] to transform the depth data of the back-view depth images to the front-view camera coordinate system. The root-mean-square error of the calibration is 1.27 cm.…”

Section: B Actual Depth Image Datasetmentioning

confidence: 99%

CNN-Based Denoising, Completion, and Prediction of Whole-Body Human-Depth Images

2019

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Three-dimensional human shape reconstruction is important in many applications, such as virtual or augmented reality (VR/AR), virtual clothing fitting, and healthcare. In this paper, we propose a learning-based method for reconstructing a whole-body point cloud from a single front-view humandepth image. Because actual depth images typically suffer from noise and missing data, an accurate point cloud cannot be reasonably obtained by simply predicting a back-view depth image. To solve this problem, we propose to use convolutional neural networks that not only predict a back-view depth image but also refine the input front-view depth image. To train the networks, we propose a carefully designed method for generating synthetic but realistic human-depth images with noise and missing data. Experiments show that the proposed method is effective for obtaining seamless whole-body point clouds. In addition, the experiments show that the networks trained on the synthetic depth images are ready for application to actual depth images. INDEX TERMS 3D human shape, convolutional neural networks, deep learning, single depth image, synthetic data generation.

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Section: B Actual Depth Image Datasetmentioning

confidence: 99%

CNN-Based Denoising, Completion, and Prediction of Whole-Body Human-Depth Images

2019

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“…If the frame rate of the RGB-D video is high, the identity transformation can be regarded as the initial estimate [1]. For the multiview system, either extrinsic calibration [6,7] or global registration algorithms [8][9][10][11] can be employed.…”

Section: Introductionmentioning

confidence: 99%

Colored Point Cloud Registration by Depth Filtering

Choi

Hwang

2021

Sensors

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In the last stage of colored point cloud registration, depth measurement errors hinder the achievement of accurate and visually plausible alignments. Recently, an algorithm has been proposed to extend the Iterative Closest Point (ICP) algorithm to refine the measured depth values instead of the pose between point clouds. However, the algorithm suffers from numerical instability, so a postprocessing step is needed to restrict erroneous output depth values. In this paper, we present a new algorithm with improved numerical stability. Unlike the previous algorithm heavily relying on point-to-plane distances, our algorithm constructs a cost function based on an adaptive combination of two different projected distances to prevent numerical instability. We address the problem of registering a source point cloud to the union of the source and reference point clouds. This extension allows all source points to be processed in a unified filtering framework, irrespective of the existence of their corresponding points in the reference point cloud. The extension also improves the numerical stability of using the point-to-plane distances. The experiments show that the proposed algorithm improves the registration accuracy and provides high-quality alignments of colored point clouds.

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Robust alternating optimisation for extrinsic calibration of RGB‐D cameras

Cited by 2 publications

References 7 publications

CNN-Based Denoising, Completion, and Prediction of Whole-Body Human-Depth Images

CNN-Based Denoising, Completion, and Prediction of Whole-Body Human-Depth Images

Colored Point Cloud Registration by Depth Filtering

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