TY - GEN
T1 - Automatic Spatial Template Generation for Realistic 3D Modeling of Large-Scale Indoor Spaces
AU - Hyeon, Janghun
AU - Choi, Hyunga
AU - Kim, Joohyung
AU - Jang, Bumchul
AU - Kang, Jaehyeon
AU - Doh, Nakju
N1 - Funding Information:
This research was supported by the Brain Korea 21 Plus project in 2019, a Korean Evaluation Institute of Industrial Technology(KEIT) Grant(No.10073166) funded by the Ministry of Trade Industry and Energy(MOTIE), and the grant (19NSIP-B135746-03) from the National Spatial Information Research Program (NSIP) funded by the Ministry of Land, Infrastructure, and Transport of the Korean government.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - This paper proposes a realistic indoor modeling framework for large-scale indoor spaces. The proposed framework reduces the geometric complexity of an indoor model to efficiently represent large-scale environments for image-based rendering (IBR) approaches. For this purpose, the proposed framework removes geometrically excluded objects (GEOs) in point cloud and images, which represent the primary factors in high geometric complexity. In particular, GEOs are coherently removed from all images using a global geometry model. Then, the remaining holes are inpainted using globally consistent guidelines, to achieve accurate image blending in IBR approaches. The experimental results verify that the proposed GEO removal framework provides efficient point clouds and images for realistic indoor modeling in large-scale indoor spaces.
AB - This paper proposes a realistic indoor modeling framework for large-scale indoor spaces. The proposed framework reduces the geometric complexity of an indoor model to efficiently represent large-scale environments for image-based rendering (IBR) approaches. For this purpose, the proposed framework removes geometrically excluded objects (GEOs) in point cloud and images, which represent the primary factors in high geometric complexity. In particular, GEOs are coherently removed from all images using a global geometry model. Then, the remaining holes are inpainted using globally consistent guidelines, to achieve accurate image blending in IBR approaches. The experimental results verify that the proposed GEO removal framework provides efficient point clouds and images for realistic indoor modeling in large-scale indoor spaces.
UR - http://www.scopus.com/inward/record.url?scp=85081166318&partnerID=8YFLogxK
U2 - 10.1109/IROS40897.2019.8968286
DO - 10.1109/IROS40897.2019.8968286
M3 - Conference contribution
AN - SCOPUS:85081166318
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 4221
EP - 4228
BT - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Y2 - 3 November 2019 through 8 November 2019
ER -