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2018 | OriginalPaper | Buchkapitel

ArticulatedFusion: Real-Time Reconstruction of Motion, Geometry and Segmentation Using a Single Depth Camera

verfasst von : Chao Li, Zheheng Zhao, Xiaohu Guo

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

This paper proposes a real-time dynamic scene reconstruction method capable of reproducing the motion, geometry, and segmentation simultaneously given live depth stream from a single RGB-D camera. Our approach fuses geometry frame by frame and uses a segmentation-enhanced node graph structure to drive the deformation of geometry in registration step. A two-level node motion optimization is proposed. The optimization space of node motions and the range of physically-plausible deformations are largely reduced by taking advantage of the articulated motion prior, which is solved by an efficient node graph segmentation method. Compared to previous fusion-based dynamic scene reconstruction methods, our experiments show robust and improved reconstruction results for tangential and occluded motions.

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Vorheriges Kapitel Extreme Network Compression via Filter Group Approximation

Nächstes Kapitel MRF Optimization with Separable Convex Prior on Partially Ordered Labels

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Titel: ArticulatedFusion: Real-Time Reconstruction of Motion, Geometry and Segmentation Using a Single Depth Camera
verfasst von: Chao Li
Zheheng Zhao
Xiaohu Guo
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01236-6

Electronic ISBN: 978-3-030-01237-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01237-3_20

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