  We propose a dense neural simultaneous localization and mapping (SLAM)
approach for monocular RGBD input which anchors the features of a neural scene
representation in a point cloud that is iteratively generated in an
input-dependent data-driven manner. We demonstrate that both tracking and
mapping can be performed with the same point-based neural scene representation
by minimizing an RGBD-based re-rendering loss. In contrast to recent dense
neural SLAM methods which anchor the scene features in a sparse grid, our
point-based approach allows dynamically adapting the anchor point density to
the information density of the input. This strategy reduces runtime and memory
usage in regions with fewer details and dedicates higher point density to
resolve fine details. Our approach performs either better or competitive to
existing dense neural RGBD SLAM methods in tracking, mapping and rendering
accuracy on the Replica, TUM-RGBD and ScanNet datasets. The source code is
available at https://github.com/eriksandstroem/Point-SLAM.
