Abstract
Reflectance and shape are two important components in visually perceiving the real world. Inferring the reflectance and shape of an object through cameras is a fundamental research topic in the field of computer vision. While three-dimensional shape recovery is pervasive with varieties of approaches and practical applications, reflectance recovery has only emerged recently. Reflectance recovery is a challenging task that is usually conducted in controlled environments, such as a laboratory environment with a special apparatus. However, it is desirable that the reflectance be recovered in the field with a handy camera so that reflectance can be jointly recovered with the shape. To that end, we present a solution that simultaneously recovers the reflectance and shape (i.e., dense depth and normal maps) of an object under natural illumination with commercially available handy cameras. We employ a light field camera to capture one light field image of the object, and a 360-degree camera to capture the illumination. The proposed method provides positive results in both simulation and real-world experiments.
| Original language | English |
|---|---|
| Pages (from-to) | 1707-1722 |
| Number of pages | 16 |
| Journal | International Journal of Computer Vision |
| Volume | 127 |
| Issue number | 11-12 |
| DOIs | |
| Publication status | Published - Dec 1 2019 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
- Artificial Intelligence
Cite this
Reflectance and Shape Estimation with a Light Field Camera Under Natural Illumination. / Ngo, Thanh Trung; Nagahara, Hajime; Nishino, Ko; Taniguchi, Rin ichiro; Yagi, Yasushi.
In: International Journal of Computer Vision, Vol. 127, No. 11-12, 01.12.2019, p. 1707-1722.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Reflectance and Shape Estimation with a Light Field Camera Under Natural Illumination
AU - Ngo, Thanh Trung
AU - Nagahara, Hajime
AU - Nishino, Ko
AU - Taniguchi, Rin ichiro
AU - Yagi, Yasushi
PY - 2019/12/1
Y1 - 2019/12/1
N2 - Reflectance and shape are two important components in visually perceiving the real world. Inferring the reflectance and shape of an object through cameras is a fundamental research topic in the field of computer vision. While three-dimensional shape recovery is pervasive with varieties of approaches and practical applications, reflectance recovery has only emerged recently. Reflectance recovery is a challenging task that is usually conducted in controlled environments, such as a laboratory environment with a special apparatus. However, it is desirable that the reflectance be recovered in the field with a handy camera so that reflectance can be jointly recovered with the shape. To that end, we present a solution that simultaneously recovers the reflectance and shape (i.e., dense depth and normal maps) of an object under natural illumination with commercially available handy cameras. We employ a light field camera to capture one light field image of the object, and a 360-degree camera to capture the illumination. The proposed method provides positive results in both simulation and real-world experiments.
AB - Reflectance and shape are two important components in visually perceiving the real world. Inferring the reflectance and shape of an object through cameras is a fundamental research topic in the field of computer vision. While three-dimensional shape recovery is pervasive with varieties of approaches and practical applications, reflectance recovery has only emerged recently. Reflectance recovery is a challenging task that is usually conducted in controlled environments, such as a laboratory environment with a special apparatus. However, it is desirable that the reflectance be recovered in the field with a handy camera so that reflectance can be jointly recovered with the shape. To that end, we present a solution that simultaneously recovers the reflectance and shape (i.e., dense depth and normal maps) of an object under natural illumination with commercially available handy cameras. We employ a light field camera to capture one light field image of the object, and a 360-degree camera to capture the illumination. The proposed method provides positive results in both simulation and real-world experiments.
UR - http://www.scopus.com/inward/record.url?scp=85061361639&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061361639&partnerID=8YFLogxK
U2 - 10.1007/s11263-019-01149-5
DO - 10.1007/s11263-019-01149-5
M3 - Article
AN - SCOPUS:85061361639
VL - 127
SP - 1707
EP - 1722
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
SN - 0920-5691
IS - 11-12
ER -