  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.
