Joint Layout Estimation and Global Multi-View Registration for Indoor Reconstruction

Jeong-Kyun Lee, Jaewon Yea, Min-Gyu Park, Kuk-Jin Yoon; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 162-171

Abstract


In this paper, we propose an approach to jointly solve scene layout estimation and global registration problems for accurate indoor 3D reconstruction. Given a sequence of range data, we build a set of scene fragments using KinectFusion and register them through pose graph optimization. Afterwards, we alternate layout estimation and layout-based global registration processes in iterative fashion to complement each other. We extract the scene layout through hierarchical agglomerative clustering and energy-based multi-model fitting in consideration of noisy measurements. Having the estimated scene layout in one hand, we register all the range data through the global iterative closest point algorithm where the positions of 3D points that belong to the layout such as walls and a ceiling are constrained to be close to the layout. We experimentally verify the proposed method with the publicly available synthetic and real-world datasets in both quantitative and qualitative ways.

Related Material


[pdf] [Supp] [arXiv]
[bibtex]
@InProceedings{Lee_2017_ICCV,
author = {Lee, Jeong-Kyun and Yea, Jaewon and Park, Min-Gyu and Yoon, Kuk-Jin},
title = {Joint Layout Estimation and Global Multi-View Registration for Indoor Reconstruction},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}