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

NICE-SLAM: Neural Implicit Scalable Encoding for SLAM