摘要
为了高效、高精度、低成本地实现对物体的全视角三维重建,提出一种使用深度相机融合光照约束实现全视角三维重建的方法。该重建方法中,在进行单帧重建时采用RGBD深度图像融合明暗恢复形状(Shape from shading,SFS)的重建方法,即在原有的深度数据上加上额外的光照约束来优化深度值;在相邻两帧配准时,采用快速点特征直方图(Fast point feature histograms,FPFH)特征进行匹配并通过随机采样一致性(Random sample consensus,RANSAC)滤除错误的匹配点对求解粗配准矩阵,然后通过迭代最近点(Iterative closest point,ICP)算法进行精配准得出两帧间的配准矩阵;在进行全视角的三维重建时,采用光束平差法优化相机位姿,从而消除累积误差使首尾帧完全重合,最后融合生成一个完整的模型。该方法融入了物体表面的光照信息,因此生成的三维模型更为光顺,也包含了更多物体表面的细节信息,提高了重建精度;同时该方法仅通过单张照片就能在自然光环境下完成对多反射率三维物体的重建,适用范围更广。本文方法的整个实验过程通过手持深度相机就能完成,不需要借助转台,操作更加方便。
In order to achieve efficient,high precision and inexpensive full view 3D reconstruction,a method of full-view 3D reconstruction by fusing depth camera and illumination constraints is proposed.In the single frame reconstruction,a method of 3D reconstruction by fusing RGBD and shape from shading(SFS)is used,that is,the illumination constraints are added in the original depth data to optimize the depth value.In the registration of two adjacent frames,fast point feature histogram(FPFH)features are used for matching and filtering out the wrong matching points by random sample consensus(RANSAC)algorithm.Then the transformation relation between cameras is obtained through iterative closest point(ICP)algorithm.In the full angle of 3D reconstruction,the bundle adjustment is used to optimize the position and pose of the camera in order to solve the problem that the first and last frames can not be completely overlapped by the cumulative error.Finally,a complete model is generated.The method integrates the illumination information of the surface of the object,therefore,the generated 3D model is smoother,and contains more detailed information of the surface of the object,which improves the reconstruction accuracy.The method can complete the reconstruction of multi-reflectivity 3D objects in a natural light environment with a single photo,and has a wider application range.The entire experiment can be carried out with a handheld depth camera,which makes it easier to operate without turntable.
作者
李健
杨苏
刘富强
何斌
LI Jian;YANG Su;LIU Fuqiang;HE Bin(School of Electrical Information and Artificial Intelligence,Shaanxi University of Science and Technology,Xi’an,710021,China;Department of Electrical and Information Engineering,Tongji University,Shanghai,201804,China)
出处
《数据采集与处理》
CSCD
北大核心
2020年第1期53-64,共12页
Journal of Data Acquisition and Processing
基金
国家重点研发计划(2018YFB1305300)资助项目
国家自然科学基金(61825303)资助项目
