Skip to Main content Skip to Navigation
Conference papers

Adaptive Structure from Motion with a contrario model estimation

Pierre Moulon 1, 2, 3, * Pascal Monasse 1, 2, 3 Renaud Marlet 1, 2, 3, 4
* Corresponding author
1 imagine [Marne-la-Vallée]
ligm - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
Abstract : Structure from Motion (SfM) algorithms take as input multi-view stereo images (along with internal calibration information) and yield a 3D point cloud and camera orientations/poses in a common 3D coordinate system. In the case of an incremental SfM pipeline, the process requires repeated model estimations based on detected feature points: homography, fundamental and essential matrices, as well as camera poses. These estimations have a crucial impact on the quality of 3D reconstruction. We propose to improve these estimations using the a contrario methodology. While SfM pipelines usually have globally-fixed thresholds for model estimation, the a contrario principle adapts thresholds to the input data and for each model estimation. Our experiments show that adaptive thresholds reach a significantly better precision. Additionally, the user is free from having to guess thresholds or to optimistically rely on default values. There are also cases where a globally-fixed threshold policy, whatever the threshold value is, cannot provide the best accuracy, contrary to an adaptive threshold policy.
Complete list of metadatas

Cited literature [3 references]  Display  Hide  Download

https://hal-enpc.archives-ouvertes.fr/hal-00769266
Contributor : Pierre Moulon <>
Submitted on : Sunday, December 30, 2012 - 5:06:26 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:16 PM
Document(s) archivé(s) le : Sunday, December 18, 2016 - 10:48:00 AM

Identifiers

Citation

Pierre Moulon, Pascal Monasse, Renaud Marlet. Adaptive Structure from Motion with a contrario model estimation. ACCV 2012, Nov 2012, Daejeon, South Korea. pp.257-270, ⟨10.1007/978-3-642-37447-0_20⟩. ⟨hal-00769266⟩

Share

Metrics

Record views

870

Files downloads

3038