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arXiv · 2007.02108

SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes

Yang Li·Tianwei Zhang·Yoshihiko Nakamura·Tatsuya Harada
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Abstract

We present SplitFusion, a novel dense RGB-D SLAM framework that simultaneously performs tracking and dense reconstruction for both rigid and non-rigid components of the scene. SplitFusion first adopts deep learning based semantic instant segmentation technique to split the scene into rigid or non-rigid surfaces. The split surfaces are independently tracked via rigid or non-rigid ICP and reconstructed through incremental depth map fusion. Experimental results show that the proposed approach can provide not only accurate environment maps but also well-reconstructed non-rigid targets, e.g. the moving humans.

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Yang Li, Tianwei Zhang, Yoshihiko Nakamura, Tatsuya Harada. 2020-07-04. SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes. https://arxiv.org/abs/2007.02108

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