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.
CITATION STYLE
Wang, Z., & Kneip, L. (2017). Towards space carving with a hand-held camera. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10528 LNCS, pp. 47–61). Springer Verlag. https://doi.org/10.1007/978-3-319-68345-4_5
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