王玮琦, 游雄, 杨剑, 李钦. 一种改进匹配点对选取策略的ElasticFusion室内三维重建算法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(9): 1469-1477. DOI: 10.13203/j.whugis20180278
引用本文: 王玮琦, 游雄, 杨剑, 李钦. 一种改进匹配点对选取策略的ElasticFusion室内三维重建算法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(9): 1469-1477. DOI: 10.13203/j.whugis20180278
WANG Weiqi, YOU Xiong, YANG Jian, LI Qin. ElasticFusion for Indoor 3D Reconstruction with an Improved Matching Points Selection Strategy[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9): 1469-1477. DOI: 10.13203/j.whugis20180278
Citation: WANG Weiqi, YOU Xiong, YANG Jian, LI Qin. ElasticFusion for Indoor 3D Reconstruction with an Improved Matching Points Selection Strategy[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9): 1469-1477. DOI: 10.13203/j.whugis20180278

一种改进匹配点对选取策略的ElasticFusion室内三维重建算法

ElasticFusion for Indoor 3D Reconstruction with an Improved Matching Points Selection Strategy

  • 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。

     

    Abstract: Real-time and high-quality 3D reconstruction of indoor scenes has been a research focus in the field of augmented reality and robotics. However, the 3D 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 3D 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.

     

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