Name
Abstract | ||
|---|---|---|
The goal of our research is to robustly reconstruct general 3D scenes from 2D images, with application to automatic model generation in computer graphics and virtual reality. In this paper we aim at producing relatively dense and well-distributed 3D points which can subsequently be used to reconstruct the scene structure. We present novel camera calibration and scene reconstruction using scale-invariant feature points. A generic high-dimensional vector matching scheme is proposed to enhance the efficiency and reduce the computational cost while finding feature correspondences. A framework for structure and motion is also presented that better exploits the advantages of scale-invariant features. In this approach we solve the “phantom points” problem and this greatly reduces the possibility of error propagation. The whole process requires no information other than the input images. The results illustrate that our system is capable of producing accurate scene structure and realistic 3D models within a few minutes. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11919476_56 | ISVC (1) |
Keywords | Field | DocType |
better exploit,accurate scene structure,scale-invariant feature point,scene reconstruction,automatic camera calibration,scale-invariant feature,feature correspondence,automatic model generation,computational cost,computer graphics,scene structure,virtual reality,error propagation,camera calibration,computer graphic | Structure from motion,Computer vision,Virtual reality,Propagation of uncertainty,Pattern recognition,Computer science,Image processing,Augmented reality,Camera resectioning,Artificial intelligence,Invariant (mathematics),Computer graphics | Conference |
Volume | ISSN | ISBN |
4291 | 0302-9743 | 3-540-48628-3 |
Citations | PageRank | References |
6 | 0.46 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Liu | 1 | 120 | 8.04 |
| Roger J. Hubbold | 2 | 520 | 52.85 |