Abstract
We present a reconstruction pipeline for a large-scale 3D environment viewed by a single moving RGB-D camera. Our approach combines advantages of fast and direct, regularization-free depth fusion and accurate, but costly variational schemes. The scene’s depth geometry is extracted from each camera view and efficiently integrated into a large, dense grid as a truncated signed distance function, which is organized in an octree. To account for noisy real-world input data, variational range image integration is performed in local regions of the volume directly on this octree structure. We focus on algorithms which are easily parallelizable on GPUs, allowing the pipeline to be used in real-time scenarios where the user can interactively view the reconstruction and adapt camera motion as required.
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Acknowledgements
This work was supported by the ERC Starting Grant “Light Field Imaging and Analysis” (LIA 336978, FP7-2014) and the SFB Transregio 161 “Quantitative Methods for Visual Computing”.
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Marniok, N., Johannsen, O., Goldluecke, B. (2017). An Efficient Octree Design for Local Variational Range Image Fusion. In: Roth, V., Vetter, T. (eds) Pattern Recognition. GCPR 2017. Lecture Notes in Computer Science(), vol 10496. Springer, Cham. https://doi.org/10.1007/978-3-319-66709-6_32
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