“TeeVR: Spatial Template-Based Acquisition, Modeling, and Rendering of Large-Scale Indoor Spaces” by Doh, Choi, Jang, Ahn, Jung, et al. …
Conference:
Experience Type(s):
Title:
- TeeVR: Spatial Template-Based Acquisition, Modeling, and Rendering of Large-Scale Indoor Spaces
Program Title:
- New Technologies Research & Education
Organizer(s)/Presenter(s):
Description:
We demonstrate an efficient alternative to conventional image-based rendering for large-scale spaces. Our key approach is based on a spatial template, which solely includes architectural geometric primitives. The predictability of spatial template improves the efficiency of acquisition, storage, and rendering. Thereby, modeling and rendering of larger indoor spaces is possible.
References:
[1] Peter Hedman, Tobias Ritschel, George Drettakis, and Gabriel Brostow. 2016. Scalable Inside-Out Image-Based Rendering. ACM Transactions on Graphics 35, 6 (2016), 231.
[2] ChangHyun Jun, Jihwan Youn, Jongmoo Choi, Gérard Medioni, and Nakju Lett Doh. 2015. Convex Cut: A Realtime Pseudo-Structure Extraction Algorithm for 3D Point Cloud Data. In Proceedings of the 2015IEEE/RSJ International Conference on Intelligent Robots and Systems. 3922–3929.
[3] Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang. 2018. Free-Form Image Inpainting with Gated Convolution. arXiv preprint arXiv:1806.03589 (2018).
