TY - GEN
T1 - Incremental Structural Modeling Based on Geometric and Statistical Analyses
AU - Roberto, Rafael
AU - Lima, João Paulo
AU - Uchiyama, Hideaki
AU - Arth, Clemens
AU - Teichrieb, Veronica
AU - Taniguchi, Rin Ichiro
AU - Schmalstieg, DIeter
N1 - Funding Information:
The authors would like to thank CNPq (process 140898/2014-0and456800/2014-0), CAPES(process 88881.134246/2016-01)andtheAustrianFFGunderthe Matahariprojectnr. 859208forpartiallyfundingthis research.
Funding Information:
The authors would like to thank CNPq (process 140898/2014-0 and 456800/2014-0), CAPES (process 88881.134246/2016-01) and the Austrian FFG under the Matahari project nr. 859208 for partially funding this research.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/3
Y1 - 2018/5/3
N2 - Finding high-level semantic information from a point cloud is a challenging task, and it can be used in various applications. For instance, it is useful to compactly represent the scene structure and efficiently understand the scene context. This task is even more challenging when using a hand-held monocular visual SLAM system that outputs a noisy sparse point cloud. In order to tackle this issue, we propose an incremental primitive modeling method using both geometric and statistical analyses for such point cloud. The main idea is to select only reliably-modeled shapes by analyzing the geometric relationship between the point cloud and the estimated shapes. Besides that, a statistical evaluation is incorporated to filter wrongly-detected primitives in a noisy point cloud. As a result of this processing, our approach largely improved precision when compared with state of the art methods. We also show the impact of segmenting and representing a scene using primitives instead of a point cloud.
AB - Finding high-level semantic information from a point cloud is a challenging task, and it can be used in various applications. For instance, it is useful to compactly represent the scene structure and efficiently understand the scene context. This task is even more challenging when using a hand-held monocular visual SLAM system that outputs a noisy sparse point cloud. In order to tackle this issue, we propose an incremental primitive modeling method using both geometric and statistical analyses for such point cloud. The main idea is to select only reliably-modeled shapes by analyzing the geometric relationship between the point cloud and the estimated shapes. Besides that, a statistical evaluation is incorporated to filter wrongly-detected primitives in a noisy point cloud. As a result of this processing, our approach largely improved precision when compared with state of the art methods. We also show the impact of segmenting and representing a scene using primitives instead of a point cloud.
UR - http://www.scopus.com/inward/record.url?scp=85050993882&partnerID=8YFLogxK
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U2 - 10.1109/WACV.2018.00110
DO - 10.1109/WACV.2018.00110
M3 - Conference contribution
AN - SCOPUS:85050993882
T3 - Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
SP - 955
EP - 963
BT - Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Y2 - 12 March 2018 through 15 March 2018
ER -