TY - GEN
T1 - Improved Structure from Motion Using Fiducial Marker Matching
AU - DeGol, Joseph
AU - Bretl, Timothy
AU - Hoiem, Derek
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - In this paper, we present an incremental structure from motion (SfM) algorithm that significantly outperforms existing algorithms when fiducial markers are present in the scene, and that matches the performance of existing algorithms when no markers are present. Our algorithm uses markers to limit potential incorrect image matches, change the order in which images are added to the reconstruction, and enforce new bundle adjustment constraints. To validate our algorithm, we introduce a new dataset with 16 image collections of large indoor scenes with challenging characteristics (e.g., blank hallways, glass facades, brick walls) and with markers placed throughout. We show that our algorithm produces complete, accurate reconstructions on all 16 image collections, most of which cause other algorithms to fail. Further, by selectively masking fiducial markers, we show that the presence of even a small number of markers can improve the results of our algorithm.
AB - In this paper, we present an incremental structure from motion (SfM) algorithm that significantly outperforms existing algorithms when fiducial markers are present in the scene, and that matches the performance of existing algorithms when no markers are present. Our algorithm uses markers to limit potential incorrect image matches, change the order in which images are added to the reconstruction, and enforce new bundle adjustment constraints. To validate our algorithm, we introduce a new dataset with 16 image collections of large indoor scenes with challenging characteristics (e.g., blank hallways, glass facades, brick walls) and with markers placed throughout. We show that our algorithm produces complete, accurate reconstructions on all 16 image collections, most of which cause other algorithms to fail. Further, by selectively masking fiducial markers, we show that the presence of even a small number of markers can improve the results of our algorithm.
KW - 3D reconstruction
KW - Fiducial markers
KW - SFM
KW - SLAM
KW - Simultaneous localization and mapping
KW - Structure from motion
UR - http://www.scopus.com/inward/record.url?scp=85055103881&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055103881&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01219-9_17
DO - 10.1007/978-3-030-01219-9_17
M3 - Conference contribution
AN - SCOPUS:85055103881
SN - 9783030012182
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 281
EP - 296
BT - Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
A2 - Ferrari, Vittorio
A2 - Sminchisescu, Cristian
A2 - Hebert, Martial
A2 - Weiss, Yair
PB - Springer
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -