id |
caadria2022_405 |
authors |
Onishi, Ryo, Fukuda, Tomohiro and Yabuki, Nobuyoshi |
year |
2022 |
title |
A Remote Sharing Method of 3D Physical Objects Using Instance-Segmented Real-Time 3D Point Cloud for Design Meeting |
source |
Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 395-404 |
doi |
https://doi.org/10.52842/conf.caadria.2022.2.395
|
summary |
In the field of architecture and urban design, physical models are used in design meetings. Furthermore, teleconferencing via the internet has begun to be widely used in society due to COVID-19 and in preparation for disasters. Although conventional web conferencing can share only 2D information through screens, it is expected that interactive screen sharing of physical objects will enable smoother remote conferencing. A system that can manipulate point clouds in clusters by dividing real-time point clouds captured from 3D real objects by distance has been reported as a way to share physical objects. However, because the point clouds are divided by distance between the two clusters when the point clouds get closer than some threshold, they become treated as a single object. In this study, we aim to develop a system that uses instance segmentation to divide point clouds by region rather than by distance between objects. This system is expected to contribute to the realisation of better architectural and urban design processes without any misunderstandings among the parties involved and to the reduction of unnecessary energy consumption due to travel for face-to-face meetings. |
keywords |
remote meeting, fast point cloud, instance segmentation, three-dimensional remote sharing, mixed reality, SDG 11, SDG 13 |
series |
CAADRIA |
email |
|
full text |
file.pdf (959,295 bytes) |
references |
Content-type: text/plain
|
Bolya, D., Zhou, C., Xiao, F. & Lee, Y. J. (2019)
YOLACT: Real-time instance segmentation
, Proceedings of the IEEE/CVF International Conference on Computer Vision8ICCV (pp. 9157-9166)
|
|
|
|
Bolya, D., Zhou, C., Xiao, F. & Lee, Y. J. (2019)
YOLACT++: Better Real-time Instance Segmentation
, IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(2), 1108-1121. https://doi.org/ 10.1109/TPAMI.2020.3014297
|
|
|
|
Chen, J. Y. C. & Thropp, J. E. (2007)
Review of Low Frame Rate Effects on Human Performance
, IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans, 37(6), 1063-1076
|
|
|
|
He, K., Gkioxari, G. & Dollar, P. (2017)
Mask R-CNN
, IEEE International Conference on Computer Vision (ICCV) 2017 (pp. 2980–2988). https://doi.org/10.1109/ICCV.2017.322
|
|
|
|
Ishikawa, D., Fukuda, T. & Yabuki, N. (2020)
A Mixed Reality Coordinate System for Multiple HMD Users Manipulating Real-time Point Cloud Objects - Towards virtual and interactive 3D synchronous sharing of physical objects in teleconference during design study
, Proceedings of the 38th eCAADe Conference (pp. 197-206)
|
|
|
|
Minsky, M. (1980)
Telepresence
, OMNI Magazine, June, 44-52
|
|
|
|
Qi, C. R., Su, H., Mo, K. & Guibas, L. J. (2017)
PointNet++: Deep hierarchical feature learning on point sets in a metric space
, 31st Advances Neural Information Processing Systems (NIPS 2017) (pp. 5099-5108)
|
|
|
|
Stotko, P., Krumpen, S., Hullin, M. B., Weinmann, M. & Klein, R. (2019)
SLAMCast: Large-scale, real-time 3D reconstruction and streaming for immersive multi-client live telepresence
, IEEE transactions on visualization and computer graphics, 25(5), 2102-2112
|
|
|
|
Yan, X. & Crookes, R. J. (2009)
Reduction potentials of energy demand and GHG emissions in China's road transport sector
, Energy Policy, 37(2), 658-668. https://doi.org/10.1016/j.enpol.2008.10.008Yang, B., Wang, J., Clark, R., Hu, Q., Wang, S., Markham, A., & Trigoni, N. (20). Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. In 33rd International Conference on Neural Information Processing Systems (pp. 2940-2949)
|
|
|
|
last changed |
2022/07/22 07:34 |
|