Generation of photorealistic 3D image using optical digitizer

Liu, X. M.; Peng, Xiang; Yin, Yongkai; Li, A. M.; Liu, X. L.; Wu, Wei

doi:10.1364/ao.51.001304

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…A “priming” (Euclidean) transformation (constant for each device) was then applied to the digitization data from each of the three devices to place the MRI facial surface data and the digitization data from each device in a similar coordinate frame. Following this, for each participant and each digitization system, a K-D tree (Bulan and Ozturk, 2001 ) iterative closest point (ICP) algorithm (Besl and Mckay, 1992 ) with point-to-plane error minimization (Chen and Medioni, 1992 ) was used for automatic co-registration of MRI and digitization data. For the laser scanner and Kinect sensor, the scalp surface and digitization data were restricted to include the face data above the upper lip and below the hairline only.…”

Section: Methodsmentioning

confidence: 99%

Faster and improved 3-D head digitization in MEG using Kinect

et al. 2014

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Accuracy in localizing the brain areas that generate neuromagnetic activity in magnetoencephalography (MEG) is dependent on properly co-registering MEG data to the participant's structural magnetic resonance image (MRI). Effective MEG-MRI co-registration is, in turn, dependent on how accurately we can digitize anatomical landmarks on the surface of the head. In this study, we compared the performance of three devices—Polhemus electromagnetic system, NextEngine laser scanner and Microsoft Kinect for Windows—for source localization accuracy and MEG-MRI co-registration. A calibrated phantom was used for verifying the source localization accuracy. The Kinect improved source localization accuracy over the Polhemus and the laser scanner by 2.23 mm (137%) and 0.81 mm (50%), respectively. MEG-MRI co-registration accuracy was verified on data from five healthy human participants, who received the digitization process using all three devices. The Kinect device captured approximately 2000 times more surface points than the Polhemus in one third of the time (1 min compared to 3 min) and thrice as many points as the NextEngine laser scanner. Following automated surface matching, the calculated mean MEG-MRI co-registration error for the Kinect was improved by 2.85 mm with respect to the Polhemus device, and equivalent to the laser scanner. Importantly, the Kinect device automatically aligns 20–30 images per second in real-time, reducing the limitations on participant head movement during digitization that are implicit in the NextEngine laser scan (~1 min). We conclude that the Kinect scanner is an effective device for head digitization in MEG, providing the necessary accuracy in source localization and MEG-MRI co-registration, while reducing digitization time.

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Section: Methodsmentioning

confidence: 99%

Faster and improved 3-D head digitization in MEG using Kinect

et al. 2014

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“…A photorealistic model of a real-life object or a scene has a great potential in a number of application areas such as game development, computer animation, film production, virtual reality, cultural heritage protection, digital content creation, and three-dimensional television (3DTV), to name just a few. In comparison with the generation of geometric models that have received much attentions in past decades, the issue of generating three-dimensional (3D) textured models or photorealistic 3D images becomes increasingly interesting in recent years with the advancement of various 3D optical digitizers that bridge real life to a virtual world [17,18]. Furthermore, the dimension and shape of an object can be represented through a geometric model whereas the natural appearance of the object is represented with color texture.…”

Section: Creation Of Photorealistic 3d Imagementioning

confidence: 99%

“…One approach for texture blending is to weight the colors of texture pixels according to some specific rules; others have adopted image mosaic technology and achieved texture blending by removing the uncontinuity of mosaic patches. Recently we proposed alternative approach to generate an accurate and photorealistic model [18 ]. In this approach, we acquire the range images and associated texture images from different perspectives with dedicated 3D optical sensor.…”

Section: Creation Of Photorealistic 3d Imagementioning

confidence: 99%

“…In this approach, we acquire the range images and associated texture images from different perspectives with dedicated 3D optical sensor. For more technical details, the readers may refer to Ref [18] and references therein. Here we adopt the same strategy on MN-3D where the cameras, 3D node sensors, and whole MN-3D have been accurately calibrated in a hierarchical way as described in Subsection 2.4.…”

Section: Creation Of Photorealistic 3d Imagementioning

confidence: 99%

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Active 3D Imaging Based on White Light Scanning: from Single Sensor to Sensor Networks

Peng¹,

Chen²,

He³

et al. 2017

Proceedings of 3DBODY.TECH 2017 - 8th International Conference and Exhibition On 3D Body Scanning and Processing Technologies,

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Active 3D imaging based on white light scanning is a sort of efficient techniques which can digitize objects existed in physical world, and therefore to acquire 3D images of test targets. A light coding with a series of structured light patterns, for example, fringe patterns are usually applied for such a 3D imaging modality. A single optical 3D sensor based on fringe projection or 3D optical sensor network composed of multiple node sensors can be regarded as kinds of optical instruments that works on principle of computational imaging. In order to reconstruct a 3D image it is necessary to decode the fringe pattern to get the phase map that acts as special marks encoding each point of test object or scene, resulting in accurate determination of point correspondence. Once the homologous pairs identified precisely a 3D image can be obtained through a series of computations using a stereoscopic vision theory. This paper will briefly reviewed the working principle of an optical digitizer based on the strategy of phase-aided active imaging (PAAI) and that of 3D sensor network composed of multiple PAAI sensors. In addition, many potential applications in 3D body scanning, face scanning, and heritage digitizing will also be addressed in this paper to show great potentials of 3D optical digitizer based on the PAAI strategy and PAAI-based optical measurement network. Both academic research at the Shenzhen University and commercialized products developed at the Shenzhen ESUN Co. Ltd. with number of patented technologies will be presented.

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“…In some cases, the light of the scene is controlled, so it is assumed that the scene is scanned under constant illumination. This assumption greatly simplifies the colour integration method which consists merely of searching for optimal blending functions [11–14]. For non-controlled scenarios, researchers have come up with only partial solutions.…”

Section: Introductionmentioning

confidence: 99%

Geometric and Colour Data Fusion for Outdoor 3D Models

Merchán

Adán

Salamanca

et al. 2012

Sensors

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This paper deals with the generation of accurate, dense and coloured 3D models of outdoor scenarios from scanners. This is a challenging research field in which several problems still remain unsolved. In particular, the process of 3D model creation in outdoor scenes may be inefficient if the scene is digitalized under unsuitable technical (specific scanner on-board camera) and environmental (rain, dampness, changing illumination) conditions. We address our research towards the integration of images and range data to produce photorealistic models. Our proposal is based on decoupling the colour integration and geometry reconstruction stages, making them independent and controlled processes. This issue is approached from two different viewpoints. On the one hand, given a complete model (geometry plus texture), we propose a method to modify the original texture provided by the scanner on-board camera with the colour information extracted from external images taken at given moments and under specific environmental conditions. On the other hand, we propose an algorithm to directly assign external images onto the complete geometric model, thus avoiding tedious on-line calibration processes. We present the work conducted on two large Roman archaeological sites dating from the first century A.D., namely, the Theatre of Segobriga and the Fori Porticus of Emerita Augusta, both in Spain. The results obtained demonstrate that our approach could be useful in the digitalization and 3D modelling fields.

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Generation of photorealistic 3D image using optical digitizer

Cited by 10 publications

References 26 publications

Faster and improved 3-D head digitization in MEG using Kinect

Faster and improved 3-D head digitization in MEG using Kinect

Active 3D Imaging Based on White Light Scanning: from Single Sensor to Sensor Networks

Geometric and Colour Data Fusion for Outdoor 3D Models

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