Abstract
We extended the well known Kinect Fusion approach [11] with a particle filter framework to improve the tracking of abrupt camera movements, while the estimated camera pose is further refined with the ICP algorithm. All performance-critical algorithms were implemented on modern graphics hardware using the CUDA GPGPU language and are largely parallelized.
It has been shown that our procedure has only minimal reduced precision compared to known techniques, but provides higher robustness against abrupt camera movements and dynamic occlusions. Furthermore the algorithm runs at a frame-time of approx. 24.6098 ms on modern hardware, hence enabling real time capability.
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Höhner, N., Hebborn, A.K., Müller, S. (2018). Particle Filter Based Tracking and Mapping. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_27
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DOI: https://doi.org/10.1007/978-3-030-03801-4_27
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