Self-calibrated, Multi-spectral Photometric Stereo for 3D Face Capture
Abstract This paper addresses the problem of obtaining 3d detailed reconstructions of human faces in real-time and with inexpensive hardware. We present an algorithm based on a monocular multi-spectral photometric-stereo setup. This system is known to capture high-detailed deforming 3d surfaces at h...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Vogiatzis, George [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2011 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of computer vision - Springer US, 1987, 97(2011), 1 vom: 06. Aug., Seite 91-103 |
|---|---|
| Übergeordnetes Werk: |
volume:97 ; year:2011 ; number:1 ; day:06 ; month:08 ; pages:91-103 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11263-011-0482-7 |
|---|
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OLC2057746178 |
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| 520 | |a Abstract This paper addresses the problem of obtaining 3d detailed reconstructions of human faces in real-time and with inexpensive hardware. We present an algorithm based on a monocular multi-spectral photometric-stereo setup. This system is known to capture high-detailed deforming 3d surfaces at high frame rates and without having to use any expensive hardware or synchronized light stage. However, the main challenge of such a setup is the calibration stage, which depends on the lights setup and how they interact with the specific material being captured, in this case, human faces. For this purpose we develop a self-calibration technique where the person being captured is asked to perform a rigid motion in front of the camera, maintaining a neutral expression. Rigidity constrains are then used to compute the head’s motion with a structure-from-motion algorithm. Once the motion is obtained, a multi-view stereo algorithm reconstructs a coarse 3d model of the face. This coarse model is then used to estimate the lighting parameters with a stratified approach: In the first step we use a RANSAC search to identify purely diffuse points on the face and to simultaneously estimate this diffuse reflectance model. In the second step we apply non-linear optimization to fit a non-Lambertian reflectance model to the outliers of the previous step. The calibration procedure is validated with synthetic and real data. | ||
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10.1007/s11263-011-0482-7 doi (DE-627)OLC2057746178 (DE-He213)s11263-011-0482-7-p DE-627 ger DE-627 rakwb eng 004 VZ Vogiatzis, George verfasserin aut Self-calibrated, Multi-spectral Photometric Stereo for 3D Face Capture 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract This paper addresses the problem of obtaining 3d detailed reconstructions of human faces in real-time and with inexpensive hardware. We present an algorithm based on a monocular multi-spectral photometric-stereo setup. This system is known to capture high-detailed deforming 3d surfaces at high frame rates and without having to use any expensive hardware or synchronized light stage. However, the main challenge of such a setup is the calibration stage, which depends on the lights setup and how they interact with the specific material being captured, in this case, human faces. For this purpose we develop a self-calibration technique where the person being captured is asked to perform a rigid motion in front of the camera, maintaining a neutral expression. Rigidity constrains are then used to compute the head’s motion with a structure-from-motion algorithm. Once the motion is obtained, a multi-view stereo algorithm reconstructs a coarse 3d model of the face. This coarse model is then used to estimate the lighting parameters with a stratified approach: In the first step we use a RANSAC search to identify purely diffuse points on the face and to simultaneously estimate this diffuse reflectance model. In the second step we apply non-linear optimization to fit a non-Lambertian reflectance model to the outliers of the previous step. The calibration procedure is validated with synthetic and real data. Photometric stereo Multi-spectral Faces Motion capture Calibration Hernández, Carlos aut Enthalten in International journal of computer vision Springer US, 1987 97(2011), 1 vom: 06. Aug., Seite 91-103 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:97 year:2011 number:1 day:06 month:08 pages:91-103 https://doi.org/10.1007/s11263-011-0482-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 97 2011 1 06 08 91-103 |
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10.1007/s11263-011-0482-7 doi (DE-627)OLC2057746178 (DE-He213)s11263-011-0482-7-p DE-627 ger DE-627 rakwb eng 004 VZ Vogiatzis, George verfasserin aut Self-calibrated, Multi-spectral Photometric Stereo for 3D Face Capture 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract This paper addresses the problem of obtaining 3d detailed reconstructions of human faces in real-time and with inexpensive hardware. We present an algorithm based on a monocular multi-spectral photometric-stereo setup. This system is known to capture high-detailed deforming 3d surfaces at high frame rates and without having to use any expensive hardware or synchronized light stage. However, the main challenge of such a setup is the calibration stage, which depends on the lights setup and how they interact with the specific material being captured, in this case, human faces. For this purpose we develop a self-calibration technique where the person being captured is asked to perform a rigid motion in front of the camera, maintaining a neutral expression. Rigidity constrains are then used to compute the head’s motion with a structure-from-motion algorithm. Once the motion is obtained, a multi-view stereo algorithm reconstructs a coarse 3d model of the face. This coarse model is then used to estimate the lighting parameters with a stratified approach: In the first step we use a RANSAC search to identify purely diffuse points on the face and to simultaneously estimate this diffuse reflectance model. In the second step we apply non-linear optimization to fit a non-Lambertian reflectance model to the outliers of the previous step. The calibration procedure is validated with synthetic and real data. Photometric stereo Multi-spectral Faces Motion capture Calibration Hernández, Carlos aut Enthalten in International journal of computer vision Springer US, 1987 97(2011), 1 vom: 06. Aug., Seite 91-103 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:97 year:2011 number:1 day:06 month:08 pages:91-103 https://doi.org/10.1007/s11263-011-0482-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 97 2011 1 06 08 91-103 |
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10.1007/s11263-011-0482-7 doi (DE-627)OLC2057746178 (DE-He213)s11263-011-0482-7-p DE-627 ger DE-627 rakwb eng 004 VZ Vogiatzis, George verfasserin aut Self-calibrated, Multi-spectral Photometric Stereo for 3D Face Capture 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract This paper addresses the problem of obtaining 3d detailed reconstructions of human faces in real-time and with inexpensive hardware. We present an algorithm based on a monocular multi-spectral photometric-stereo setup. This system is known to capture high-detailed deforming 3d surfaces at high frame rates and without having to use any expensive hardware or synchronized light stage. However, the main challenge of such a setup is the calibration stage, which depends on the lights setup and how they interact with the specific material being captured, in this case, human faces. For this purpose we develop a self-calibration technique where the person being captured is asked to perform a rigid motion in front of the camera, maintaining a neutral expression. Rigidity constrains are then used to compute the head’s motion with a structure-from-motion algorithm. Once the motion is obtained, a multi-view stereo algorithm reconstructs a coarse 3d model of the face. This coarse model is then used to estimate the lighting parameters with a stratified approach: In the first step we use a RANSAC search to identify purely diffuse points on the face and to simultaneously estimate this diffuse reflectance model. In the second step we apply non-linear optimization to fit a non-Lambertian reflectance model to the outliers of the previous step. The calibration procedure is validated with synthetic and real data. Photometric stereo Multi-spectral Faces Motion capture Calibration Hernández, Carlos aut Enthalten in International journal of computer vision Springer US, 1987 97(2011), 1 vom: 06. Aug., Seite 91-103 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:97 year:2011 number:1 day:06 month:08 pages:91-103 https://doi.org/10.1007/s11263-011-0482-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 97 2011 1 06 08 91-103 |
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Vogiatzis, George Hernández, Carlos |
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10.1007/s11263-011-0482-7 |
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004 |
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self-calibrated, multi-spectral photometric stereo for 3d face capture |
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Self-calibrated, Multi-spectral Photometric Stereo for 3D Face Capture |
| abstract |
Abstract This paper addresses the problem of obtaining 3d detailed reconstructions of human faces in real-time and with inexpensive hardware. We present an algorithm based on a monocular multi-spectral photometric-stereo setup. This system is known to capture high-detailed deforming 3d surfaces at high frame rates and without having to use any expensive hardware or synchronized light stage. However, the main challenge of such a setup is the calibration stage, which depends on the lights setup and how they interact with the specific material being captured, in this case, human faces. For this purpose we develop a self-calibration technique where the person being captured is asked to perform a rigid motion in front of the camera, maintaining a neutral expression. Rigidity constrains are then used to compute the head’s motion with a structure-from-motion algorithm. Once the motion is obtained, a multi-view stereo algorithm reconstructs a coarse 3d model of the face. This coarse model is then used to estimate the lighting parameters with a stratified approach: In the first step we use a RANSAC search to identify purely diffuse points on the face and to simultaneously estimate this diffuse reflectance model. In the second step we apply non-linear optimization to fit a non-Lambertian reflectance model to the outliers of the previous step. The calibration procedure is validated with synthetic and real data. © Springer Science+Business Media, LLC 2011 |
| abstractGer |
Abstract This paper addresses the problem of obtaining 3d detailed reconstructions of human faces in real-time and with inexpensive hardware. We present an algorithm based on a monocular multi-spectral photometric-stereo setup. This system is known to capture high-detailed deforming 3d surfaces at high frame rates and without having to use any expensive hardware or synchronized light stage. However, the main challenge of such a setup is the calibration stage, which depends on the lights setup and how they interact with the specific material being captured, in this case, human faces. For this purpose we develop a self-calibration technique where the person being captured is asked to perform a rigid motion in front of the camera, maintaining a neutral expression. Rigidity constrains are then used to compute the head’s motion with a structure-from-motion algorithm. Once the motion is obtained, a multi-view stereo algorithm reconstructs a coarse 3d model of the face. This coarse model is then used to estimate the lighting parameters with a stratified approach: In the first step we use a RANSAC search to identify purely diffuse points on the face and to simultaneously estimate this diffuse reflectance model. In the second step we apply non-linear optimization to fit a non-Lambertian reflectance model to the outliers of the previous step. The calibration procedure is validated with synthetic and real data. © Springer Science+Business Media, LLC 2011 |
| abstract_unstemmed |
Abstract This paper addresses the problem of obtaining 3d detailed reconstructions of human faces in real-time and with inexpensive hardware. We present an algorithm based on a monocular multi-spectral photometric-stereo setup. This system is known to capture high-detailed deforming 3d surfaces at high frame rates and without having to use any expensive hardware or synchronized light stage. However, the main challenge of such a setup is the calibration stage, which depends on the lights setup and how they interact with the specific material being captured, in this case, human faces. For this purpose we develop a self-calibration technique where the person being captured is asked to perform a rigid motion in front of the camera, maintaining a neutral expression. Rigidity constrains are then used to compute the head’s motion with a structure-from-motion algorithm. Once the motion is obtained, a multi-view stereo algorithm reconstructs a coarse 3d model of the face. This coarse model is then used to estimate the lighting parameters with a stratified approach: In the first step we use a RANSAC search to identify purely diffuse points on the face and to simultaneously estimate this diffuse reflectance model. In the second step we apply non-linear optimization to fit a non-Lambertian reflectance model to the outliers of the previous step. The calibration procedure is validated with synthetic and real data. © Springer Science+Business Media, LLC 2011 |
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Self-calibrated, Multi-spectral Photometric Stereo for 3D Face Capture |
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