Comparison of low cost 3D structured light scanners for face modeling

Bakırman, Tolga; Gümüşay, Mustafa Ümit; Reis, Hatice Çatal; Selbesoglu, Mahmut Oguz; Yosmaoglu, Serra; Yaras, Mehmet Cem; Şeker, Dursun Zafer; Bayram, Bülent

doi:10.1364/ao.56.000985

Cited by 32 publications

(15 citation statements)

References 40 publications

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“…The clinical CT scan obtains a 3D image of a patient who may wear landmarks such as a firmly-attached helmet. A boundary detection algorithm extracts the facial/helmet surface and key points for mesh generation as a basis for image registration [32]. The fact is utilized that the inner ear and the face surface are in a rigid relation.…”

Section: Spatial Resolutionmentioning

confidence: 99%

Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning

Zheng

Cong

et al. 2020

IEEE Access

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While micro-CT systems are instrumental in preclinical research, clinical micro-CT imaging has long been desired with cochlear implantation as a primary application. The structural details of the cochlear implant and the temporal bone require a significantly higher image resolution than that (about 0.2 mm) provided by current medical CT scanners. In this paper, we propose a clinical micro-CT (CMCT) system design integrating conventional spiral cone-beam CT, contemporary interior tomography, deep learning techniques, and the technologies of a micro-focus X-ray source, a photon-counting detector (PCD), and robotic arms for ultrahigh-resolution localized tomography of a freely-selected volume of interest (VOI) at a minimized radiation dose level. The whole system consists of a standard CT scanner for a clinical CT exam and VOI specification, and a robotic micro-CT scanner for a local scan of high spatial and spectral resolution at minimized radiation dose. The prior information from the global scan is also fully utilized for background compensation of the local scan data for accurate and stable VOI reconstruction. Our results and analysis show that the proposed hybrid reconstruction algorithm delivers accurate high-resolution local reconstruction, and is insensitive to the misalignment of the isocenter position, initial view angle and scale mismatch in the data/image registration. These findings demonstrate the feasibility of our system design. We envision that deep learning techniques can be leveraged for optimized imaging performance. With highresolution imaging, high dose efficiency and low system cost synergistically, our proposed CMCT system has great promise in temporal bone imaging as well as various other clinical applications.INDEX TERMS Clinical micro-CT, Deep learning, High-resolution imaging, Interior tomography, Photoncounting detector, Robotic arms, Temporal bone imaging, X-ray computed tomography This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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Section: Spatial Resolutionmentioning

confidence: 99%

Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning

Zheng

Cong

et al. 2020

IEEE Access

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“…[1][2][3][4][5][6] In the dot-pattern structured light sensing, the depth of each spot on the measured object can be accurately measured, which is very suitable for 3D point cloud profilometry. [7][8][9][10][11] The way of measuring with projected dotpattern structured light is very robust to the interference of ambient light, which is suitable for practical applications. 12,13 Compared with traditional projector projection, dot-pattern laser projection has the advantages of smaller volume, lower cost, and easier integration.…”

Section: Introductionmentioning

confidence: 99%

“…The noncontact optical measurement method is recognized as the most promising 3D profilometry method due to its advantages of high sensitivity, no damage, and many data acquisition 1‐6 . In the dot‐pattern structured light sensing, the depth of each spot on the measured object can be accurately measured, which is very suitable for 3D point cloud profilometry 7‐11 . The way of measuring with projected dot‐pattern structured light is very robust to the interference of ambient light, which is suitable for practical applications 12,13 .…”

Section: Introductionmentioning

confidence: 99%

Time‐resolved coded structured light for 3D measurement

Zhou

2020

Micro & Optical Tech Letters

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Corresponding points matching is the fundamental computational task underlying stereo vision, and the key issue in making stereo vision practical is to find the most suitable algorithm to quickly match corresponding points, which leads to 3D reconstruction. In this paper, we proposed a new encoding strategy of dot-pattern structured light based on temporal modulated vertical cavity surface emitting laser (VCSEL) array, which is especially suitable for the dot projector in miniaturized devices for stereo vision. Although VCSEL has been used in Apple's iPhone X for dot projector, the time dimension information is not used. VCSEL array has the capability of controlling each light source separately, so we can encode the information of time dimension into the structured light, which improves the stereo-matching accuracy and simplifies the algorithm. In addition, compared to the temporal random speckle structured light method, this time-resolved coded dot-pattern structured light method greatly reduces the number of frames of projecting images required, which has great significance for real-time 3D reconstruction in practical applications. Therefore, the method proposed here could be of high interest for its potentials in 3D measurement in mobile phones and other miniaturized devices. The proof-of-principle experiment results verify the computational efficiency and matching accuracy of the method.

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“…Profile measurement is one of the most important techniques in the optical inspection and vision 1–4 . Recently, information acquirement of the 3D profile is popular in the research fields of the automotive industry 5 , the robot vision 6 , the medical diagnosis 7 , the reverse engineering 8 , etc. However, the camera only generates 2D images without depth information, which restricts us to acquire and perceive the profile of object in the real world.…”

Section: Introductionmentioning

confidence: 99%

Profile measurement adopting binocular active vision with normalization object of vector orthogonality

Chen

et al. 2019

Sci Rep

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Active-vision-based measurement plays an important role in the profile inspection study. The binocular vision, a passive vision, is employed in the active vision system to contribute the benefits of them. The laser plane is calibrated by two 2D targets without texture initially. Then, an L target with feature points is designed to construct the orthogonality object of two vectors. In order to accurately model the binocular-active-vision system, the feature points on the L target are built by two cameras and parameterized by the laser plane. Different from the optimization methods on the basis of the distance object, the laser plane is further refined by the distance-angle object. Thus, an optimization function is created considering both the norms and angles of the vectors. However, the scale of the distance is diverse from the scale of the angle. Therefore, the optimization function is enhanced by the normalization process to balance the different scales. The comparison experiments show that the binocular active vision with the normalization object of vector orthogonality achieves the decreasing distance errors of 25%, 22%, 13% and 4%, as well as the decreasing angle errors of 23%, 20%, 14% and 4%, which indicates an accurate measurement to reconstruct the object profile.

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Comparison of low cost 3D structured light scanners for face modeling

Cited by 32 publications

References 40 publications

Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning

Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning

Time‐resolved coded structured light for 3D measurement

Profile measurement adopting binocular active vision with normalization object of vector orthogonality

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