Name
Abstract | ||
|---|---|---|
A robust progressive structure from motion (PSFM) method is proposed for unordered images. Our method can reduce accumulative error efficiently during scene dense recovery and camera motion estimation. The whole unordered images are divided into two classes: key frames and non-key frames. For key frames, superior features are selected and tracked to initialize the parameters of PSFM reliably and reduce the accumulative errors. During the implementation of PSFM, several novel and efficient strategies are proposed to detect the parameter estimation errors and remove the outliers in feature tracking. A local on-demand scheme is proposed to dramatically reduce the computing cost of sparse bundle adjustment. The experimental results from different scenes show that our method is efficient and robust in scene dense recovery and camera motion estimation. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ROBIO.2012.6490946 | ROBIO |
Keywords | Field | DocType |
parameter estimation,sparse bundle adjustment,key frame class,feature tracking,motion estimation,feature extraction,unordered image,psfm parameter,unsupervised progressive structure-from-motion,feature selection,scene dense recovery,nonkey frame class,unsupervised learning,psfm method,camera motion estimation | Structure from motion,Computer vision,Pattern recognition,Computer science,Bundle adjustment,Outlier,Artificial intelligence,Estimation theory,Motion estimation,Feature tracking | Conference |
ISBN | Citations | PageRank |
978-1-4673-2125-9 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingkui Du | 1 | 17 | 7.23 |
| Baojie Fan | 2 | 41 | 10.48 |
| Y. Tang | 3 | 243 | 33.69 |
| Jianda Han | 4 | 220 | 60.61 |