@article{Li2022, 
author = {Jianwei Li and Wei Gao and Yihong Wu and Yangdong Liu and Yanfei Shen},
title = {High-quality indoor scene 3D reconstruction with RGB-D cameras: A brief review},
year = {2022},
journal = {Computational Visual Media},
volume = {8},
number = {3},
pages = {369-393},
keywords = {3D reconstruction, image processing, camera pose estimation, surface fusion},
url = {https://www.sciopen.com/article/10.1007/s41095-021-0250-8},
doi = {10.1007/s41095-021-0250-8},
abstract = {High-quality 3D reconstruction is an important topic in computer graphics and computer vision with many applications, such as robotics and augmented reality. The advent of consumer RGB-D cameras has made a profound advance in indoor scenereconstruction. For the past few years, researchers have spent significant effort to develop algorithms to capture 3D models with RGB-D cameras. As depth images produced by consumer RGB-D cameras are noisy and incomplete when surfaces are shiny, bright, transparent, or far from the camera, obtaining high- quality 3D scene models is still a challenge for existing systems. We here review high-quality 3D indoor scene reconstruction methods using consumer RGB-D cameras. In this paper, we make comparisons and analyses from the following aspects: (i) depth processing methods in 3D reconstruction are reviewed in terms of enhancement and completion, (ii) ICP-based, feature-based, and hybrid methods of camera pose estimation methods are reviewed, and (iii) surface reconstruction methods are reviewed in terms of surface fusion, optimization, and completion. The performance of state-of-the-art methods is also compared and analyzed. This survey will be useful for researchers who want to follow best practices in designing new high-quality 3D reconstruction methods.}
}