TY - GEN
T1 - GCN-Calculated Graph-Feature Embedding for 3D Endoscopic System Based on Active Stereo
AU - Mikamo, Michihiro
AU - Kawasaki, Hiroshi
AU - Sagawa, Ryusuke
AU - Furukawa, Ryo
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - One of the promising fields for active-stereo sensors is medical applications such as 3D endoscope systems. For such systems, robust correspondence estimation between the detected patterns and the projected pattern is the most crucial. In this paper, we propose an auto-calibrating 3D endoscopic system using a 2D grid-graph pattern, where codes are embedded into each grid point. Since the pattern is a grid graph, we use a graph convolutional network (GCN) to calculate node-wise embedding accumulating code information of nearby grid points in the graph. The correspondence estimation using the GCN-calculated feature embedding is shown to be stable, even without using epipolar constraints. Using the correspondence estimation, we show that the auto-calibrating 3D measurement system can be realized. In the experiment, we confirmed that the proposed system achieved high accuracy and robust estimation comparing to the previous methods.
AB - One of the promising fields for active-stereo sensors is medical applications such as 3D endoscope systems. For such systems, robust correspondence estimation between the detected patterns and the projected pattern is the most crucial. In this paper, we propose an auto-calibrating 3D endoscopic system using a 2D grid-graph pattern, where codes are embedded into each grid point. Since the pattern is a grid graph, we use a graph convolutional network (GCN) to calculate node-wise embedding accumulating code information of nearby grid points in the graph. The correspondence estimation using the GCN-calculated feature embedding is shown to be stable, even without using epipolar constraints. Using the correspondence estimation, we show that the auto-calibrating 3D measurement system can be realized. In the experiment, we confirmed that the proposed system achieved high accuracy and robust estimation comparing to the previous methods.
UR - http://www.scopus.com/inward/record.url?scp=85112715844&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112715844&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-81638-4_21
DO - 10.1007/978-3-030-81638-4_21
M3 - Conference contribution
AN - SCOPUS:85112715844
SN - 9783030816377
T3 - Communications in Computer and Information Science
SP - 253
EP - 266
BT - Frontiers of Computer Vision - 27th International Workshop, IW-FCV 2021, Revised Selected Papers
A2 - Jeong, Hieyong
A2 - Sumi, Kazuhiko
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th International Workshop on Frontiers of Computer Vision, IW-FCV 2021
Y2 - 22 February 2021 through 23 February 2021
ER -