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Augmenting depth estimation from deep convolutional neural network using multi-spectral photometric stereo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Augmenting depth estimation from deep convolutional neural network using multi-spectral photometric stereo. / Luo, Yisong; Jiao, Hengchao; Qi, Lin; Dong, Junyu; Zhang, Shu; Yu, Hui.

2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2018. 17878684.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Luo, Y, Jiao, H, Qi, L, Dong, J, Zhang, S & Yu, H 2018, Augmenting depth estimation from deep convolutional neural network using multi-spectral photometric stereo. in 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)., 17878684, IEEE, 14th IEEE International Conference on Ubiquitous Intelligence and Computing, San Francisco, United States, 4/08/17. https://doi.org/10.1109/UIC-ATC.2017.8397464

APA

Luo, Y., Jiao, H., Qi, L., Dong, J., Zhang, S., & Yu, H. (2018). Augmenting depth estimation from deep convolutional neural network using multi-spectral photometric stereo. In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) [17878684] IEEE. https://doi.org/10.1109/UIC-ATC.2017.8397464

Vancouver

Luo Y, Jiao H, Qi L, Dong J, Zhang S, Yu H. Augmenting depth estimation from deep convolutional neural network using multi-spectral photometric stereo. In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE. 2018. 17878684 https://doi.org/10.1109/UIC-ATC.2017.8397464

Author

Luo, Yisong ; Jiao, Hengchao ; Qi, Lin ; Dong, Junyu ; Zhang, Shu ; Yu, Hui. / Augmenting depth estimation from deep convolutional neural network using multi-spectral photometric stereo. 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2018.

Bibtex

@inproceedings{fe8aa6e0ceef4fb1bf0df2f95e8381f1,
title = "Augmenting depth estimation from deep convolutional neural network using multi-spectral photometric stereo",
abstract = "Multi-Spectral Photometric Stereo can recover surface normals from a single image, but requires an initial estimate of the normals due to the tangle of the illumination, reflectance and camera responses on each of the RGB channels. Instead of employing a depth sensor or binocular stereo device, in this paper, we propose a method to estimate fine-scale geometry structures with the popular Deep Convolutional Neural Networks (CNNs). We train the network with rendered images of synthetic 3D objects, and apply the trained model with real world data. The CNN is used to estimate a rough prediction of the depth, then the normals from Multi-Spectral Photometric Stereo are progressively refined accordingly. Experiments demonstrate the competitive results of our method for improving the depth estimation.",
author = "Yisong Luo and Hengchao Jiao and Lin Qi and Junyu Dong and Shu Zhang and Hui Yu",
year = "2018",
month = "6",
day = "28",
doi = "10.1109/UIC-ATC.2017.8397464",
language = "English",
isbn = "978-1-5386-1591-1",
booktitle = "2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Augmenting depth estimation from deep convolutional neural network using multi-spectral photometric stereo

AU - Luo, Yisong

AU - Jiao, Hengchao

AU - Qi, Lin

AU - Dong, Junyu

AU - Zhang, Shu

AU - Yu, Hui

PY - 2018/6/28

Y1 - 2018/6/28

N2 - Multi-Spectral Photometric Stereo can recover surface normals from a single image, but requires an initial estimate of the normals due to the tangle of the illumination, reflectance and camera responses on each of the RGB channels. Instead of employing a depth sensor or binocular stereo device, in this paper, we propose a method to estimate fine-scale geometry structures with the popular Deep Convolutional Neural Networks (CNNs). We train the network with rendered images of synthetic 3D objects, and apply the trained model with real world data. The CNN is used to estimate a rough prediction of the depth, then the normals from Multi-Spectral Photometric Stereo are progressively refined accordingly. Experiments demonstrate the competitive results of our method for improving the depth estimation.

AB - Multi-Spectral Photometric Stereo can recover surface normals from a single image, but requires an initial estimate of the normals due to the tangle of the illumination, reflectance and camera responses on each of the RGB channels. Instead of employing a depth sensor or binocular stereo device, in this paper, we propose a method to estimate fine-scale geometry structures with the popular Deep Convolutional Neural Networks (CNNs). We train the network with rendered images of synthetic 3D objects, and apply the trained model with real world data. The CNN is used to estimate a rough prediction of the depth, then the normals from Multi-Spectral Photometric Stereo are progressively refined accordingly. Experiments demonstrate the competitive results of our method for improving the depth estimation.

UR - http://smart-city-sjsu.net//conference/docs/UIC-Conference-Program-Final.htm

U2 - 10.1109/UIC-ATC.2017.8397464

DO - 10.1109/UIC-ATC.2017.8397464

M3 - Conference contribution

SN - 978-1-5386-1591-1

BT - 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)

PB - IEEE

ER -

ID: 7993775