论文标题
拆分：非刚性场景的同时跟踪和映射
SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes
论文作者
论文摘要
我们提出了SplitFusion，这是一种新颖的RGB-D SLAM框架，同时对场景的刚性和非刚性组件都执行跟踪和密集的重建。 SplitFusion首先采用基于深度学习的语义即时分割技术，将场景分为刚性或非刚性表面。通过刚性或非刚性ICP独立跟踪分裂表面，并通过增量深度图融合重建。实验结果表明，所提出的方法不仅可以提供准确的环境图，还可以提供精心构造的非刚性目标，例如移动的人类。
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.
