Arbitrarily shaped objects relighting using an RGB-D camera

Takuya Ikeda, Francois De Sorbier, Hideo Saito

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

2 Citations (Scopus)

Abstract

Usually, relighting techniques require knowledge about the shape of the target object and the lighting environment. The quality of the result is highly dependent on the normals of the object because they are used in the computation of the illumination. In this paper, we propose a new relighting approach for arbitrarily shaped objects using an RGB-D camera such as the Microsoft's Kinect. The depth map is useful to estimate the normals of the object, but can be inaccurate because of the noise such as discrete depth values or missing data. An accurate segmentation of the target region for relighting is also an open issue since the boundaries in the depth map does not always match color's ones. We focus on the depth map modification to segment the object region and normal estimation for accurate relighting. In our experiments, we adapted some normal estimation methods from modified depth map and evaluated the accuracy of the relighting results. We discuss the effectiveness of relighting approach for an arbitrarily shaped object and the possibility of a real time relighting.

Original languageEnglish
Title of host publicationProceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013
PublisherIEEE Computer Society
Pages631-636
Number of pages6
DOIs
Publication statusPublished - 2013
Event2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013 - Naha, Okinawa, Japan
Duration: 2013 Nov 52013 Nov 8

Other

Other2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013
CountryJapan
CityNaha, Okinawa
Period13/11/513/11/8

Fingerprint

Cameras
Lighting
Color
Experiments

Keywords

  • GPU
  • Relighitng
  • RGB-D camera

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Ikeda, T., De Sorbier, F., & Saito, H. (2013). Arbitrarily shaped objects relighting using an RGB-D camera. In Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013 (pp. 631-636). [6778395] IEEE Computer Society. https://doi.org/10.1109/ACPR.2013.68

Arbitrarily shaped objects relighting using an RGB-D camera. / Ikeda, Takuya; De Sorbier, Francois; Saito, Hideo.

Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013. IEEE Computer Society, 2013. p. 631-636 6778395.

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

Ikeda, T, De Sorbier, F & Saito, H 2013, Arbitrarily shaped objects relighting using an RGB-D camera. in Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013., 6778395, IEEE Computer Society, pp. 631-636, 2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013, Naha, Okinawa, Japan, 13/11/5. https://doi.org/10.1109/ACPR.2013.68
Ikeda T, De Sorbier F, Saito H. Arbitrarily shaped objects relighting using an RGB-D camera. In Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013. IEEE Computer Society. 2013. p. 631-636. 6778395 https://doi.org/10.1109/ACPR.2013.68
Ikeda, Takuya ; De Sorbier, Francois ; Saito, Hideo. / Arbitrarily shaped objects relighting using an RGB-D camera. Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013. IEEE Computer Society, 2013. pp. 631-636
@inproceedings{d0d09e4095ff46ecb6a492eb7fd7f483,
title = "Arbitrarily shaped objects relighting using an RGB-D camera",
abstract = "Usually, relighting techniques require knowledge about the shape of the target object and the lighting environment. The quality of the result is highly dependent on the normals of the object because they are used in the computation of the illumination. In this paper, we propose a new relighting approach for arbitrarily shaped objects using an RGB-D camera such as the Microsoft's Kinect. The depth map is useful to estimate the normals of the object, but can be inaccurate because of the noise such as discrete depth values or missing data. An accurate segmentation of the target region for relighting is also an open issue since the boundaries in the depth map does not always match color's ones. We focus on the depth map modification to segment the object region and normal estimation for accurate relighting. In our experiments, we adapted some normal estimation methods from modified depth map and evaluated the accuracy of the relighting results. We discuss the effectiveness of relighting approach for an arbitrarily shaped object and the possibility of a real time relighting.",
keywords = "GPU, Relighitng, RGB-D camera",
author = "Takuya Ikeda and {De Sorbier}, Francois and Hideo Saito",
year = "2013",
doi = "10.1109/ACPR.2013.68",
language = "English",
pages = "631--636",
booktitle = "Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013",
publisher = "IEEE Computer Society",

}

TY - GEN

T1 - Arbitrarily shaped objects relighting using an RGB-D camera

AU - Ikeda, Takuya

AU - De Sorbier, Francois

AU - Saito, Hideo

PY - 2013

Y1 - 2013

N2 - Usually, relighting techniques require knowledge about the shape of the target object and the lighting environment. The quality of the result is highly dependent on the normals of the object because they are used in the computation of the illumination. In this paper, we propose a new relighting approach for arbitrarily shaped objects using an RGB-D camera such as the Microsoft's Kinect. The depth map is useful to estimate the normals of the object, but can be inaccurate because of the noise such as discrete depth values or missing data. An accurate segmentation of the target region for relighting is also an open issue since the boundaries in the depth map does not always match color's ones. We focus on the depth map modification to segment the object region and normal estimation for accurate relighting. In our experiments, we adapted some normal estimation methods from modified depth map and evaluated the accuracy of the relighting results. We discuss the effectiveness of relighting approach for an arbitrarily shaped object and the possibility of a real time relighting.

AB - Usually, relighting techniques require knowledge about the shape of the target object and the lighting environment. The quality of the result is highly dependent on the normals of the object because they are used in the computation of the illumination. In this paper, we propose a new relighting approach for arbitrarily shaped objects using an RGB-D camera such as the Microsoft's Kinect. The depth map is useful to estimate the normals of the object, but can be inaccurate because of the noise such as discrete depth values or missing data. An accurate segmentation of the target region for relighting is also an open issue since the boundaries in the depth map does not always match color's ones. We focus on the depth map modification to segment the object region and normal estimation for accurate relighting. In our experiments, we adapted some normal estimation methods from modified depth map and evaluated the accuracy of the relighting results. We discuss the effectiveness of relighting approach for an arbitrarily shaped object and the possibility of a real time relighting.

KW - GPU

KW - Relighitng

KW - RGB-D camera

UR - http://www.scopus.com/inward/record.url?scp=84899058573&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84899058573&partnerID=8YFLogxK

U2 - 10.1109/ACPR.2013.68

DO - 10.1109/ACPR.2013.68

M3 - Conference contribution

AN - SCOPUS:84899058573

SP - 631

EP - 636

BT - Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013

PB - IEEE Computer Society

ER -