TY - GEN
T1 - Semantic Object Selection and Detection for Diminished Reality Based on SLAM with Viewpoint Class
AU - Nakajima, Yoshikatsu
AU - Mori, Shohei
AU - Saito, Hideo
N1 - Funding Information:
This research presentation is supported in part by a research assistantship of a Grant-in-Aid to the Program for Leading Graduate School for “Science for Development of Super Mature Society” from MEXT in Japan.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/27
Y1 - 2017/10/27
N2 - We propose a novel diminished reality method which is able to (i) automatically recognize the region to be diminished, (ii) work with a single RGB-D sensor, and (iii) work without pre-processing to generate a 3D model of the target scene by utilizing SLAM, segmentation, and recognition framework. Especially, regarding the recognition of the area to be diminished, our method is able to maintain high accuracy no matter how the camera moves by distributing the viewpoints for each object uniformly and aggregating recognition results from each distributed viewpoint as the same weight. These advantages are demonstrated on the UW RGB-D Dataset and Scenes.
AB - We propose a novel diminished reality method which is able to (i) automatically recognize the region to be diminished, (ii) work with a single RGB-D sensor, and (iii) work without pre-processing to generate a 3D model of the target scene by utilizing SLAM, segmentation, and recognition framework. Especially, regarding the recognition of the area to be diminished, our method is able to maintain high accuracy no matter how the camera moves by distributing the viewpoints for each object uniformly and aggregating recognition results from each distributed viewpoint as the same weight. These advantages are demonstrated on the UW RGB-D Dataset and Scenes.
KW - Convolutional Neural Network
KW - Diminished Reality
KW - Object Recognition
KW - SLAM
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85040242065&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040242065&partnerID=8YFLogxK
U2 - 10.1109/ISMAR-Adjunct.2017.98
DO - 10.1109/ISMAR-Adjunct.2017.98
M3 - Conference contribution
AN - SCOPUS:85040242065
T3 - Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017
SP - 338
EP - 343
BT - Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017
A2 - Broll, Wolfgang
A2 - Regenbrecht, Holger
A2 - Bruder, Gerd
A2 - Servieres, Myriam
A2 - Sugimoto, Maki
A2 - Swan, J Edward
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th Adjunct IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017
Y2 - 9 October 2017 through 13 October 2017
ER -