摘要
对室内场景进行实时高质量的三维重建是机器人、增强现实等领域关注的重点。目前基于RGB-D传感器的三维重建方法存在局部模型重建效果差、点云模型包含孔洞等问题。而影响三维模型重建效果的主要因素有两个,一是由点云配准解算出的位姿参数精度,二是闭环检测准确程度。对此,在保证算法实时性的基础上,通过改进迭代最近点算法(iterative closest point algorithm,ICP)中匹配点的选取策略,提升模型重建效果。并利用径向基函数构建隐式曲面的方式对点云模型中的孔洞进行事后修补。选用ICL-NUIM等公开数据集进行实验验证,结果表明,改进后的算法在模型重建效果以及相机轨迹估计方面均有显著提升。
Real-time and high-quality 3 D reconstruction of indoor scenes has been a research focus in the field of augmented reality and robotics.However,the 3 D reconstruction methods using RGB-D sensors suffer from weaknesses such as poor local model quality and producing holes in points cloud models.The two key factors that affect the quality of 3 D reconstruction are the accuracy of the pose parameters derived from points cloud registration and the accuracy of loop closure.Firstly,an improved ElasticFusion algorithm is proposed to achieve better reconstruction quality in real time,which is achieved by improving the strategy of searching matching points in the iterative closest point(ICP)algorithm.Furthermore,radial basis functions are used to construct implicit surfaces in order to fill holes in the point clouds models that are generated in the previous step.In the end,benchmarks such as ICL-NUIM are used to evaluate the proposed algorithm.The experimental results have shown that our algorithm can significantly improve the quality of model reconstruction and the accuracy of the camera trajectory estimation.
作者
王玮琦
游雄
杨剑
李钦
WANG Weiqi;YOU Xiong;YANG Jian;LI Qin(Institute of Geospatial Information,Information Engineering University,Zhengzhou 450052,China)
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2020年第9期1469-1477,共9页
Geomatics and Information Science of Wuhan University
基金
国家重点研发计划(2017YFB0503500)
河南省科技创新计划(142101510005)
国家自然科学基金青年基金(41801317)。
