Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 427-434 |
| Seitenumfang | 8 |
| Fachzeitschrift | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Jahrgang | II-3/W5 |
| Publikationsstatus | Veröffentlicht - 20 Aug. 2015 |
| Veranstaltung | ISPRS Geospatial Week 2015 - La Grande Motte, Frankreich Dauer: 28 Sept. 2015 → 3 Okt. 2015 |
Abstract
driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method.
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Erdkunde und Planetologie (sonstige)
- Umweltwissenschaften (insg.)
- Umweltwissenschaften (sonstige)
- Physik und Astronomie (insg.)
- Instrumentierung
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in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jahrgang II-3/W5, 20.08.2015, S. 427-434.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Joint 3D estimation of vehicles and scene flow
AU - Menze, M.
AU - Heipke, C.
AU - Geiger, A.
PY - 2015/8/20
Y1 - 2015/8/20
N2 - driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method.
AB - driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method.
KW - 3D Reconstruction
KW - Active Shape Model
KW - Motion Estimation
KW - Object Detection
KW - Scene Flow
UR - http://www.scopus.com/inward/record.url?scp=85014234386&partnerID=8YFLogxK
U2 - 10.5194/isprsannals-II-3-W5-427-2015
DO - 10.5194/isprsannals-II-3-W5-427-2015
M3 - Conference article
AN - SCOPUS:85014234386
VL - II-3/W5
SP - 427
EP - 434
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
T2 - ISPRS Geospatial Week 2015
Y2 - 28 September 2015 through 3 October 2015
ER -