FCN-Based 3D Reconstruction with Multi-Source Photometric Stereo

As a classical method widely used in 3D reconstruction tasks, the multi-source Photometric Stereo can obtain more accurate 3D reconstruction results compared with the basic Photometric Stereo, but its complex calibration and solution process reduces the efficiency of this algorithm. In this paper, we propose a multi-source Photometric Stereo 3D reconstruction method based on the fully convolutional network (FCN). We first represent the 3D shape of the object as a depth value corresponding to each pixel as the optimized object. After training in an end-to-end manner, our network can efficiently obtain 3D information on the object surface. In addition, we added two regularization constraints to the general loss function, which can effectively help the network to optimize. Under the same light source configuration, our method can obtain a higher accuracy than the classic multi-source Photometric Stereo. At the same time, our new loss function can help the deep learning method to get a more realistic 3D reconstruction result. We have also used our own real dataset to experimentally verify our method. The experimental results show that our method has a good effect on solving the main problems faced by the classical method.

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Volume Measurement Method for Irregular Objects Based on Shape from Shading

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.494-495.789 ◽

2014 ◽

Vol 494-495 ◽

pp. 789-792

Author(s):

Hong She Dang ◽

Chu Jia Guo

Keyword(s):

3D Reconstruction ◽

Measurement Method ◽

Convex Surface ◽

Shape From Shading ◽

Volume Measurement ◽

Intensity Level ◽

Reconstruction Method ◽

Regular Shape ◽

Image Acquisition System ◽

3D Information

In this paper, we propose a volume measurement method for irregular objects. And three cameras were used in the image acquisition system. In order to reduce the intensity level and be more coincident with the 3D reconstruction method, a method called Histogram Acceleration has been used. Instead of using the regular shape from shading method, the relation between intensity and the missed 3D information was found. This method is valid within a certain error range. Its showed by experiment that this method has a good performance when dealing with objects with a smooth and convex surface.

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A 3D Reconstruction Method Based on Binocular View Geometry

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.33.299 ◽

2010 ◽

Vol 33 ◽

pp. 299-303

Author(s):

Zhong Yan Liu ◽

Guo Quan Wang ◽

Dong Ping Wang

Keyword(s):

3D Reconstruction ◽

Three Dimensional ◽

Reconstruction Method ◽

Feature Points ◽

Imaging Model ◽

Extrinsic Parameters ◽

Image Acquisition System ◽

Rearview Mirror ◽

3D Information ◽

Intrinsic And Extrinsic Parameters

A method was proposed to gain three-dimensional (3D) reconstruction based on binocular view geometry. Images used to calibrate cameras and reconstruct car’s rearview mirror by image acquisition system, by calibration image, a camera's intrinsic and extrinsic parameters, projective and fundamental matrixes were drawn by Matlab7.1;the collected rearview mirror images is pretreated to draw refined laser, extracted feature points, find the very appropriate match points by epipolar geometry principle; according to the camera imaging model to calculate the coordinates of space points, display point cloud, fitting space points to reconstruct car’s rearview mirror; experimental results show this method can better restore the car’s rearview mirror of 3D information.

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Global Inhomogeneity Index Evaluation of a DCT-based EIT Lung Imaging

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3010 ◽

2020 ◽

Vol 6 (3) ◽

pp. 36-39

Author(s):

Rongqing Chen ◽

Knut Möller

Keyword(s):

Classical Method ◽

Lung Imaging ◽

Imaging Method ◽

Reconstruction Method ◽

Essential Information ◽

Preliminary Results ◽

Reconstruction Methods ◽

Comparison Results ◽

Index Evaluation ◽

Inhomogeneity Index

AbstractPurpose: To evaluate a novel structural-functional DCT-based EIT lung imaging method against the classical EIT reconstruction. Method: Taken retrospectively from a former study, EIT data was evaluated using both reconstruction methods. For different phases of ventilation, EIT images are analyzed with respect to the global inhomogeneity (GI) index for comparison. Results: A significant less variant GI index was observed in the DCTbased method, compared to the index from classical method. Conclusion: The DCT-based method generates more accurate lung contour yet decreasing the essential information in the image which affects the GI index. These preliminary results must be consolidated with more patient data in different breathing states.

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Research on 3D Reconstruction Method Based on Temporal Fourier Transform Profilometry

2020 2nd International Conference on Information Technology and Computer Application (ITCA) ◽

10.1109/itca52113.2020.00150 ◽

2020 ◽

Author(s):

Liao Min ◽

Luo weifeng

Keyword(s):

Fourier Transform ◽

3D Reconstruction ◽

Reconstruction Method ◽

Fourier Transform Profilometry

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Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients

Cancers ◽

10.3390/cancers13040786 ◽

2021 ◽

Vol 13 (4) ◽

pp. 786

Author(s):

Daniel M. Lang ◽

Jan C. Peeken ◽

Stephanie E. Combs ◽

Jan J. Wilkens ◽

Stefan Bartzsch

Keyword(s):

Deep Learning ◽

Cancer Patients ◽

Oropharyngeal Cancer ◽

Characteristic Curve ◽

Learning Models ◽

Convolutional Network ◽

Hpv Status ◽

3D Information ◽

External Test

Infection with the human papillomavirus (HPV) has been identified as a major risk factor for oropharyngeal cancer (OPC). HPV-related OPCs have been shown to be more radiosensitive and to have a reduced risk for cancer related death. Hence, the histological determination of HPV status of cancer patients depicts an essential diagnostic factor. We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets, we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine-tuned, such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases, with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet, the video pre-trained model performed best. Deep learning models are capable of CT image-based HPV status determination. Video based pre-training has the ability to improve training for 3D medical data, but further studies are needed for verification.

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3D RECONSTRUCTION OF BREAST CANCER FROM MAMMOGRAMS USING VOLUME RENDERING TECHNIQUES

Jurnal Teknologi ◽

10.11113/jt.v75.4978 ◽

2015 ◽

Vol 75 (2) ◽

Author(s):

Ho Wei Yong ◽

Abdullah Bade ◽

Rajesh Kumar Muniandy

Keyword(s):

Breast Cancer ◽

3D Reconstruction ◽

Computational Cost ◽

Reconstruction Method ◽

Cancer Model ◽

Volume Reconstruction ◽

The Past ◽

Breast Cancer Model ◽

Reconstruction Software ◽

To Come

Over the past thirty years, a number of researchers have investigated on 3D organ reconstruction from medical images and there are a few 3D reconstruction software available on the market. However, not many researcheshave focused on3D reconstruction of breast cancer’s tumours. Due to the method complexity, most 3D breast cancer’s tumours reconstruction were done based on MRI slices dataeven though mammogram is the current clinical practice for breast cancer screening. Therefore, this research will investigate the process of creating a method that will be able to reconstruct 3D breast cancer’s tumours from mammograms effectively. Several steps were proposed for this research which includes data acquisition, volume reconstruction, andvolume rendering. The expected output from this research is the 3D breast cancer’s tumours model that is generated from correctly registered mammograms. The main purpose of this research is to come up with a 3D reconstruction method that can produce good breast cancer model from mammograms while using minimal computational cost.

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