论文标题
SE（3）转化的神经隐式图算法用于重新映射功能的算法
An Algorithm for the SE(3)-Transformation on Neural Implicit Maps for Remapping Functions
论文作者
论文摘要
隐式表示由于其效率和灵活性，被广泛用于对象重建。在2021年，已经为增量重建发明了一个名为神经隐式图的新型结构。神经隐式地图减轻了以前的在线3D密集重建的效率低下的记忆成本问题，同时产生质量更高。但是，神经隐式图受到了以下限制：由于扫描框架在生成神经隐式映射后，它不支持重新映射。这意味着，这一代过程都不可逆，也不是深层的先验是可以转变的。不可易变的属性使得应用循环封闭技术是不可能的。 ％我们提出了基于神经隐式地图的转换算法来填补此空白。由于我们的神经隐式映射是可以转换的，因此我们的模型支持了此潜在特征的特殊地图的重新映射。 ％实验表明，我们的重新映射模块能够将转换神经隐含地图妥善化为新姿势。我们的映射模型嵌入了大满贯框架中，能够应对环关闭的重新映射并展示高质量的表面重建。 ％我们的实现可在GitHub \ footNote {\ url {https://github.com/jarrome/imt_mapping}}中提供研究社区。
Implicit representations are widely used for object reconstruction due to their efficiency and flexibility. In 2021, a novel structure named neural implicit map has been invented for incremental reconstruction. A neural implicit map alleviates the problem of inefficient memory cost of previous online 3D dense reconstruction while producing better quality. % However, the neural implicit map suffers the limitation that it does not support remapping as the frames of scans are encoded into a deep prior after generating the neural implicit map. This means, that neither this generation process is invertible, nor a deep prior is transformable. The non-remappable property makes it not possible to apply loop-closure techniques. % We present a neural implicit map based transformation algorithm to fill this gap. As our neural implicit map is transformable, our model supports remapping for this special map of latent features. % Experiments show that our remapping module is capable to well-transform neural implicit maps to new poses. Embedded into a SLAM framework, our mapping model is able to tackle the remapping of loop closures and demonstrates high-quality surface reconstruction. % Our implementation is available at github\footnote{\url{https://github.com/Jarrome/IMT_Mapping}} for the research community.
