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Robust dense reconstruction by range merging based on confidence estimation
文献类型：期刊论文
| 作者 | Chen, YD ; Hao, CY ; Wu, W ; Wu, EH |
| 刊名 | SCIENCE CHINA-INFORMATION SCIENCES
 |
| 出版日期 | 2016 |
| 卷号 | 59期号:9 |
| ISSN号 | 1674-733X |
| 关键词 | stereo matching 3D reconstruction textureless regions outliers details loss range map |
| 中文摘要 | Although the stereo matching problem has been extensively studied during the past decades, automatically computing a dense 3D reconstruction from several multiple views is still a difficult task owing to the problems of textureless regions, outliers, detail loss, and various other factors. In this paper, these difficult problems are handled effectively by a robust model that outputs an accurate and dense reconstruction as the final result from an input of multiple images captured by a normal camera. First, the positions of the camera and sparse 3D points are estimated by a structure-from-motion algorithm and we compute the range map with a confidence estimation for each image in our approach. Then all the range maps are integrated into a fine point cloud data set. In the final step we use a Poisson reconstruction algorithm to finish the reconstruction. The major contributions of the work lie in the following points: effective range-computation and confidence-estimation methods are proposed to handle the problems of textureless regions, outliers and detail loss. Then, the range maps are merged into the point cloud data in terms of a confidence-estimation. Finally, Poisson reconstruction algorithm completes the dense mesh. In addition, texture mapping is also implemented as a post-processing work for obtaining good visual effects. Experimental results are presented to demonstrate the effectiveness of the proposed approach. |
| 英文摘要 | Although the stereo matching problem has been extensively studied during the past decades, automatically computing a dense 3D reconstruction from several multiple views is still a difficult task owing to the problems of textureless regions, outliers, detail loss, and various other factors. In this paper, these difficult problems are handled effectively by a robust model that outputs an accurate and dense reconstruction as the final result from an input of multiple images captured by a normal camera. First, the positions of the camera and sparse 3D points are estimated by a structure-from-motion algorithm and we compute the range map with a confidence estimation for each image in our approach. Then all the range maps are integrated into a fine point cloud data set. In the final step we use a Poisson reconstruction algorithm to finish the reconstruction. The major contributions of the work lie in the following points: effective range-computation and confidence-estimation methods are proposed to handle the problems of textureless regions, outliers and detail loss. Then, the range maps are merged into the point cloud data in terms of a confidence-estimation. Finally, Poisson reconstruction algorithm completes the dense mesh. In addition, texture mapping is also implemented as a post-processing work for obtaining good visual effects. Experimental results are presented to demonstrate the effectiveness of the proposed approach. |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000381929800002 |
| 公开日期 | 2016-12-09 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/17301]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 GB/T 7714 | Chen, YD,Hao, CY,Wu, W,et al. Robust dense reconstruction by range merging based on confidence estimation[J]. SCIENCE CHINA-INFORMATION SCIENCES,2016,59(9). |
| APA | Chen, YD,Hao, CY,Wu, W,&Wu, EH.(2016).Robust dense reconstruction by range merging based on confidence estimation.SCIENCE CHINA-INFORMATION SCIENCES,59(9). |
| MLA | Chen, YD,et al."Robust dense reconstruction by range merging based on confidence estimation".SCIENCE CHINA-INFORMATION SCIENCES 59.9(2016). |
入库方式： OAI收割
来源：软件研究所
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