TY - GEN
T1 - Towards space carving with a hand-held camera
AU - Wang, Zhirui
AU - Kneip, Laurent
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - With the rise of VR applications, dense reconstruction of 3D object models becomes an increasingly important subproblem of computer vision. Most existing methods focus on the reconstruction of the actual object and assume that camera poses are known and the observed object is clearly dominant in the image. The goal of this paper is to extend these technologies to less artificial data, and enable dense 3D object modeling from an ordinary hand-held camera observing an object on top of a structured, unknown planar background. The key of our method consists of recovering highly accurate camera poses from structure from motion based on a planar scene assumption, and modeling the structure on the planar background with implicitly smooth Bezier splines. We present a complete end-to-end pipeline able to produce meaningful dense 3D models from a simple space carving approach in near real-time.
AB - With the rise of VR applications, dense reconstruction of 3D object models becomes an increasingly important subproblem of computer vision. Most existing methods focus on the reconstruction of the actual object and assume that camera poses are known and the observed object is clearly dominant in the image. The goal of this paper is to extend these technologies to less artificial data, and enable dense 3D object modeling from an ordinary hand-held camera observing an object on top of a structured, unknown planar background. The key of our method consists of recovering highly accurate camera poses from structure from motion based on a planar scene assumption, and modeling the structure on the planar background with implicitly smooth Bezier splines. We present a complete end-to-end pipeline able to produce meaningful dense 3D models from a simple space carving approach in near real-time.
UR - http://www.scopus.com/inward/record.url?scp=85031776246&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-68345-4_5
DO - 10.1007/978-3-319-68345-4_5
M3 - Conference contribution
SN - 9783319683447
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 47
EP - 61
BT - Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers
A2 - Vincze, Markus
A2 - Chen, Haoyao
A2 - Liu, Ming
PB - Springer Verlag
T2 - 11th International Conference on Computer Vision Systems, ICVS 2017
Y2 - 10 July 2017 through 13 July 2017
ER -