  Neural implicit representations have recently shown encouraging results in
various domains, including promising progress in simultaneous localization and
mapping (SLAM). Nevertheless, existing methods produce over-smoothed scene
reconstructions and have difficulty scaling up to large scenes. These
limitations are mainly due to their simple fully-connected network architecture
that does not incorporate local information in the observations. In this paper,
we present NICE-SLAM, a dense SLAM system that incorporates multi-level local
information by introducing a hierarchical scene representation. Optimizing this
representation with pre-trained geometric priors enables detailed
reconstruction on large indoor scenes. Compared to recent neural implicit SLAM
systems, our approach is more scalable, efficient, and robust. Experiments on
five challenging datasets demonstrate competitive results of NICE-SLAM in both
mapping and tracking quality. Project page:
https://pengsongyou.github.io/nice-slam
