Automatic Spatial Template Generation for Realistic 3D Modeling of Large-Scale Indoor Spaces
Janghun Hyeon, Hyunga Choi, JooHyung Kim, Bumchul Jang, Jaehyeon Kang, Nakju Doh
Abstract
This paper proposes a realistic indoor modeling framework for large-scale indoor spaces. The proposed framework reduces the geometric complexity of an indoor model to efficiently represent large-scale environments for image-based rendering (IBR) approaches. For this purpose, the proposed framework removes geometrically excluded objects (GEOs) in point cloud and images, which represent the primary factors in high geometric complexity. In particular, GEOs are coherently removed from all images using a global geometry model. Then, the remaining holes are inpainted using globally consistent guidelines, to achieve accurate image blending in IBR approaches. The experimental results verify that the proposed GEO removal framework provides efficient point clouds and images for realistic indoor modeling in large-scale indoor spaces.
BibTeX
@inproceedings{iros2019_automaticspatial,
title = {Automatic Spatial Template Generation for Realistic 3D Modeling of Large-Scale Indoor Spaces},
author = {Janghun Hyeon and Hyunga Choi and JooHyung Kim and Bumchul Jang and Jaehyeon Kang and Nakju Doh},
booktitle = {IROS 2019},
year = {2019}
}