A two-stage strategy for real-time dense 3D reconstruction of large-scale scenes

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Abstract

The frame-to-global-model approach is widely used for accurate 3D modeling from sequences of RGB-D images. Because still no perfect camera tracking system exists, the accumulation of small errors generated when registering and integrating successive RGB-D images causes deformations of the 3D model being built up. In particular, the deformations become significant when the scale of the scene to model is large. To tackle this problem, we propose a two-stage strategy to build in details a large-scale 3D model with minimal deformations where the first stage creates accurate small-scale 3D scenes in real-time from short subsequences of RGB-D images while the second stage re-organises all the results from the first stage in a geometrically consistent manner to reduce deformations as much as possible. By employing planar patches as the 3D scene representation, our proposed method runs in real-time to build accurate 3D models with minimal deformations even for large-scale scenes. Our experiments using real data confirm the effectiveness of our proposed method.

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APA

Thomas, D., & Sugimoto, A. (2015). A two-stage strategy for real-time dense 3D reconstruction of large-scale scenes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8925, pp. 428–442). Springer Verlag. https://doi.org/10.1007/978-3-319-16178-5_30

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