We show for the first time that a multilayer perceptron (MLP) can serve as the only scene representation in a real-time SLAM system for a handheld RGB-D camera. Our network is trained in live operation without prior data, building a dense, scene-specific implicit 3D model of occupancy and colour which is also immediately used for tracking. Achieving real-time SLAM via continual training of a neural network against a live image stream requires significant innovation. Our iMAP algorithm uses a keyframe structure and multi-processing computation flow, with dynamic information-guided pixel sampling for speed, with tracking at 10 Hz and global map updating at 2 Hz. The advantages of an implicit MLP over standard dense SLAM techniques include efficient geometry representation with automatic detail control and smooth, plausible filling-in of unobserved regions such as the back surfaces of objects.

翻译：我们第一次显示,多层透视器(MLP)可以作为手持 RGB-D 相机实时 SLMM 系统的唯一现场显示器。我们的网络在没有事先数据的情况下接受现场操作培训,建立了一个密集的、针对具体现场的隐含3D 占用和颜色模型,该模型也立即用于跟踪。通过不断训练神经网络对抗实况图像流实现实时SLM 需要重大创新。我们的IMAP算法使用一个关键框架结构和多处理计算流程,以动态信息引导像素取样速度快速,跟踪10赫兹,全球地图更新2赫兹。隐含的MLP相对于标准的密集 SLMM 技术的优势包括:具有自动详细控制和光滑滑的高效几何代表,可以合理填补物体背面等未观测到的区域。