Real-Time Variational Range Image Fusion and Visualization for Large-Scale Scenes Using GPU Hash Tables
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We present a real-time pipeline for large-scale 3D scene reconstruction from a single moving RGB-D camera together with interactive visualization. Our approach combines a time and space efficient data structure capable of representing large scenes, a local variational update algorithm and a visualization system. The environment's structure is reconstructed by integrating the depth image of each camera view into a sparse volume representation using a truncated signed distance function, which is organized via a hash table. Noise from real-world data is efficiently eliminated by immediately performing local variational refinements on newly integrated data. The whole pipeline is able to perform in real-time on consumer-available hardware and allows for simultaneous inspection of the currently reconstructed scene.
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MARNIOK, Nico, Bastian GOLDLÜCKE, 2018. Real-Time Variational Range Image Fusion and Visualization for Large-Scale Scenes Using GPU Hash Tables. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Tahoe, USA, 12. März 2018 - 15. März 2018. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). Piscataway, New Jersey: IEEE, 2018, pp. 912-920. ISBN 978-1-5386-5189-6. Available under: doi: 10.1109/WACV.2018.00105BibTex
@inproceedings{Marniok2018RealT-42779, year={2018}, doi={10.1109/WACV.2018.00105}, title={Real-Time Variational Range Image Fusion and Visualization for Large-Scale Scenes Using GPU Hash Tables}, isbn={978-1-5386-5189-6}, publisher={IEEE}, address={Piscataway, New Jersey}, booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)}, pages={912--920}, author={Marniok, Nico and Goldlücke, Bastian} }
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