Abstract
Image-based modeling is rapidly increasing in popularity since cameras are very affordable, widely available, and have a wide image acquisition range suitable for objects of vastly different size. In this chapter we describe a novel image-based modeling system, which produces high-quality 3D content automatically from a collection of unconstrained and uncalibrated 2D images. The system estimates camera parameters and a 3D scene geometry using Structure-from-Motion (SfM) and Bundle Adjustment techniques. The point cloud density of 3D scene components is enhanced by exploiting silhouette information of the scene. This hybrid approach dramatically improves the reconstruction of objects with few visual features. A high quality texture is created by parameterizing the reconstructed objects using a segmentation and charting approach, which also works for objects which are not homeomorphic to a sphere. The resulting parameter space contains one chart for each surface segment. A texture map is created by back projecting the best fitting input images onto each surface segment, and smoothly fusing them together over the corresponding chart by using graph-cut techniques. Our evaluation shows that our system is capable of reconstructing a wide range of objects in both indoor and outdoor environments.
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Acknowledgements
We would like to thank Prof. Eugene Zhang from the Oregon State University for providing us with code for his Feature-based Surface Parameterization technique (Zhang et al. 2005) and assisting with integrating it in our system.
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Nguyen, H.M., Wünsche, B.C., Delmas, P., Lutteroth, C., van der Mark, W. (2014). A Robust System for High-Quality Reconstruction of 3D Objects from Photographs. In: S. Hippe, Z., L. Kulikowski, J., Mroczek, T., Wtorek, J. (eds) Issues and Challenges in Artificial Intelligence. Studies in Computational Intelligence, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-319-06883-1_1
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