Abstract
This paper presents a hybrid approach for 3D object reconstruction from multiple images taken from different viewpoints. The proposed method allows to obtain a complete and automatic reconstruction from limited number of images. It begins with a sparse 3D reconstruction based on camera self-calibration and interest point matching between images. The integration of sparse approach allows us to automatically estimate the projection matrices without using a turn-table (controlled environment) often used in the Shape from Silhouette approach. In addition, it offers the possibility of an accurate estimation of the initial bounding box of the object. This bounding box is discretized into voxels afterward. Then, the reconstruction process consists in using the image Silhouettes and the photo-consistency test to finally have a volumetric textured model that can be transformed into surface model by applying the marching cube algorithm. The experiments on real data are performed to validate the proposed approach; the results indicate that our method can achieve a very satisfactory reconstruction quality.
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El Hazzat, S., Merras, M., El Akkad, N., Saaidi, A., Satori, K. (2020). Silhouettes Based-3D Object Reconstruction Using Hybrid Sparse 3D Reconstruction and Volumetric Methods. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_47
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DOI: https://doi.org/10.1007/978-981-15-0947-6_47
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