TeeVR: spatial template-based acquisition, modeling, and rendering of large-scale indoor spaces

Nathan Doh, Hyunga Choi, Bumchul Jang, Sangmin Ahn, Hyojin Jung, Sungkil Lee

Abstract: Conventional image-based rendering has limited applicability for large-scale spaces. In this study, we demonstrate an efficient alternative to conventional image-based rendering. Our key approach is based on a spatial template (ST), which solely includes architectural geometric primitives. The predictability of ST improves the efficiency of acquisition, storage, and rendering. Thereby, our system can be applied to the modeling and rendering of larger indoor spaces.