摘要
基于图像的立体重建技术直接通过多幅二维图像获取物体的三维数据模型,建模自动化程度高,且不需要任何先验信息和特殊硬件支持。但对于具有精致雕刻的中国古式建筑以及非平行拍摄的大型室外场景,现有的基于图像的三维重建技术重建模型往往存在细节信息丢失、数据散乱现象,使得重建结果不够精确。针对这一问题,综合考虑模型的光照信息、纹理阴影、凹凸感等多种特征,通过给出特征候选点匹配策略及对初始点云的可靠性排序,提出了一种多特征三维稠密重建算法MFPMVS(patch with multiple features based multi-view stereopsis)。实验表明,MFPMVS算法与经典的PMVS(patch based multi-view stereopsis)算法相比,重建得到的三维点云更加密集;凹凸感较强的模型重建细节更为细腻;仰拍得到的模型重建结果中漏洞明显减少,边缘细节信息更加完整。算法能够更稳定、鲁棒地重建出物体的三维模型,具有很高的实用价值。
3D reconstruction techniques based on images directly obtain 3D model information from multiple images. This kind of methods are with higher automaticity, moreover, they do not need any prior information and special hardware. But for Chinese ancient architectures with exquisite carving or the large outdoor scenes with non-parallel shooting, reconstruction results of existing 3D reconstruction techniques based on images may be not always promising because the details about modeling object are often missed or diffused. This paper considers comprehensively the multiple features of models such as lighting, texture, shadows and concavity, and proposes a novel algorithm for 3D reconstruction named MFPMVS （patch with multiple features based multi-view stereopsis） based on candidate fea-ture points mapping strategy and reliability sorting on initial cloud points. The experimental results show that, com-pared with the classical PMVS （patch based multi-view stereopsis） algorithm, the proposed MFPMVS algorithm can obtain more 3D point cloud, and the details of strong concavity model are more delicate. Meanwhile, the loop-holes of the reconstruction model with upward-shooting can be significantly reduced, and the edge information is more complete. More importantly, the proposed algorithm can rebuild the 3D model of the object more stably and robustly, which means the high practicability.
出处
《计算机科学与探索》
CSCD
北大核心
2015年第5期594-603,共10页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金No.61273291
山西省回国留学人员科研资助项目No.2012-008
山西省科技攻关计划项目No.20120321027-01
中国民航信息技术科研基地开放基金No.CAAC-ITRB-201305~~
