Abstract
We propose a novel pipeline for 3D reconstruction from image sequences that solely relies on dense methods. At no point sparse features are required. As input we only need a sequence of color images capturing a static scene while following a continuous path. Furthermore, we assume that an intrinsic camera calibration is known. Our pipeline comprises three steps: (1) First, we jointly estimate correspondences and stereo geometry for each two consecutive images. (2) Subsequently, we connect the individual pairwise estimates and globally refine them through bundle adjustment. As a result, all camera poses are merged into a consistent global model. This allows us to create accurate depth maps. (3) Finally, these depth maps are merged using variational range image integration techniques. Experiments show that our dense pipeline is an interesting alternative to sparse approaches. It yields accurate camera poses as well as 3D reconstructions.
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We gratefully acknowledge partial funding by the Cluster of Excellence for Multimodal Computing and Interaction.
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Schneevoigt, T., Schroers, C., Weickert, J. (2014). A Dense Pipeline for 3D Reconstruction from Image Sequences. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_52
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