Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 907-918 |
| Seitenumfang | 12 |
| Fachzeitschrift | ISPRS Journal of Photogrammetry and Remote Sensing |
| Jahrgang | 66 |
| Ausgabenummer | 6 |
| Frühes Online-Datum | 15 Aug. 2011 |
| Publikationsstatus | Veröffentlicht - Nov. 2011 |
Abstract
We present a new procedure to compute dense 3D point clouds from a sequential set of images. This procedure is considered as a second step of a three-step algorithm for 3D reconstruction from image sequences, whose first step consists of image orientation and the last step is shape reconstruction. We assume that the camera matrices as well as a sparse set of 3D points are available and we strive for obtaining a dense and reliable 3D point cloud. Three novel ideas are presented: (1) for sparse tracking and triangulation, the search space for correspondences is reduced to a line segment by means of known camera matrices and disparity ranges are provided by triangular meshes from the already available points; (2) triangular meshes from extended sets of points are used for dense matching, because these meshes help to reconstruct points in weakly textured areas and present a natural way to obtain subpixel accuracy; (3) two non-local optimization methods, namely, 1D dynamic programming along horizontal lines and semi-global optimization were employed for refinement of local results obtained from an arbitrary number of images. All methods were extensively tested on a benchmark data set and an infrared video sequence. Both visual and quantitative results demonstrate the effectiveness of our algorithm.
ASJC Scopus Sachgebiete
- Physik und Astronomie (insg.)
- Atom- und Molekularphysik sowie Optik
- Ingenieurwesen (insg.)
- Ingenieurwesen (sonstige)
- Informatik (insg.)
- Angewandte Informatik
- Erdkunde und Planetologie (insg.)
- Computer in den Geowissenschaften
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in: ISPRS Journal of Photogrammetry and Remote Sensing, Jahrgang 66, Nr. 6, 11.2011, S. 907-918.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Multi-view dense matching supported by triangular meshes
AU - Bulatov, Dimitri
AU - Wernerus, Peter
AU - Heipke, Christian
PY - 2011/11
Y1 - 2011/11
N2 - We present a new procedure to compute dense 3D point clouds from a sequential set of images. This procedure is considered as a second step of a three-step algorithm for 3D reconstruction from image sequences, whose first step consists of image orientation and the last step is shape reconstruction. We assume that the camera matrices as well as a sparse set of 3D points are available and we strive for obtaining a dense and reliable 3D point cloud. Three novel ideas are presented: (1) for sparse tracking and triangulation, the search space for correspondences is reduced to a line segment by means of known camera matrices and disparity ranges are provided by triangular meshes from the already available points; (2) triangular meshes from extended sets of points are used for dense matching, because these meshes help to reconstruct points in weakly textured areas and present a natural way to obtain subpixel accuracy; (3) two non-local optimization methods, namely, 1D dynamic programming along horizontal lines and semi-global optimization were employed for refinement of local results obtained from an arbitrary number of images. All methods were extensively tested on a benchmark data set and an infrared video sequence. Both visual and quantitative results demonstrate the effectiveness of our algorithm.
AB - We present a new procedure to compute dense 3D point clouds from a sequential set of images. This procedure is considered as a second step of a three-step algorithm for 3D reconstruction from image sequences, whose first step consists of image orientation and the last step is shape reconstruction. We assume that the camera matrices as well as a sparse set of 3D points are available and we strive for obtaining a dense and reliable 3D point cloud. Three novel ideas are presented: (1) for sparse tracking and triangulation, the search space for correspondences is reduced to a line segment by means of known camera matrices and disparity ranges are provided by triangular meshes from the already available points; (2) triangular meshes from extended sets of points are used for dense matching, because these meshes help to reconstruct points in weakly textured areas and present a natural way to obtain subpixel accuracy; (3) two non-local optimization methods, namely, 1D dynamic programming along horizontal lines and semi-global optimization were employed for refinement of local results obtained from an arbitrary number of images. All methods were extensively tested on a benchmark data set and an infrared video sequence. Both visual and quantitative results demonstrate the effectiveness of our algorithm.
KW - Depth map
KW - Matching
KW - Multi-view
KW - Semi-global
KW - Triangle mesh
UR - http://www.scopus.com/inward/record.url?scp=81555211159&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2011.06.006
DO - 10.1016/j.isprsjprs.2011.06.006
M3 - Article
AN - SCOPUS:81555211159
VL - 66
SP - 907
EP - 918
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
IS - 6
ER -