| Paper Abstract and Keywords |
| Presentation |
2026-09-04 09:50
Surface Position Estimation and Three-Dimensional Shape Reconstruction from Interference Fringe Images Using Deep Learning
Yoshiki Kuronuma, Tatsutoshi Shioda, Kohei Watabe (Saitama Univ.) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
In-line inspection of industrial products requires non-contact, high-speed measurement of three-dimensional (3D) surface shape. 2D single-shot optical tomography acquires an interference fringe image of a cross section in a single exposure without mechanical scanning; however, noise and reduced fringe contrast make fringe detection unstable and cause defects in the reconstructed 3D shape. This study formulates fringe detection as semantic segmentation of fringe regions using U-Net++ and compares it with a rule-based method that also provides pseudo labels for training. Evaluation on coin images shows
that the two methods are complementary: U-Net++ achieves a smaller RMSE of height error, while the rule-based method offers wider coverage. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
2D single-shot optical tomography / interference fringe image / fringe detection / semantic segmentation / U-Net++ / 3D shape reconstruction / / |
| Reference Info. |
ITE Tech. Rep., vol. 50, no. 24, ME2026-75, pp. 7-12, Sept. 2026. |
| Paper # |
ME2026-75 |
| Date of Issue |
2026-08-28 (ME) |
| ISSN |
Online edition: ISSN 2424-1970 |
| Download PDF |
|
| Conference Information |
| Committee |
IEICE-LOIS ME IEE-CMN |
| Conference Date |
2026-09-03 - 2026-09-04 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Kitakyushu Science and Research Park |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
|
| Paper Information |
| Registration To |
ME |
| Conference Code |
2026-09-LOIS-ME-CMN |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Surface Position Estimation and Three-Dimensional Shape Reconstruction from Interference Fringe Images Using Deep Learning |
| Sub Title (in English) |
|
| Keyword(1) |
2D single-shot optical tomography |
| Keyword(2) |
interference fringe image |
| Keyword(3) |
fringe detection |
| Keyword(4) |
semantic segmentation |
| Keyword(5) |
U-Net++ |
| Keyword(6) |
3D shape reconstruction |
| Keyword(7) |
|
| Keyword(8) |
|
| 1st Author's Name |
Yoshiki Kuronuma |
| 1st Author's Affiliation |
Saitama University (Saitama Univ.) |
| 2nd Author's Name |
Tatsutoshi Shioda |
| 2nd Author's Affiliation |
Saitama University (Saitama Univ.) |
| 3rd Author's Name |
Kohei Watabe |
| 3rd Author's Affiliation |
Saitama University (Saitama Univ.) |
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| Speaker |
Author-1 |
| Date Time |
2026-09-04 09:50:00 |
| Presentation Time |
20 minutes |
| Registration for |
ME |
| Paper # |
ME2026-75 |
| Volume (vol) |
vol.50 |
| Number (no) |
no.24 |
| Page |
pp.7-12 |
| #Pages |
6 |
| Date of Issue |
2026-08-28 (ME) |