NICE-SLAM: Neural Implicit Scalable Encoding for SLAM

Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Experiments on five challenging datasets demonstrate competitive results of NICE-SLAM in both mapping and tracking quality.

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• 1 Nevertheless, existing methods produce over- smoothed scene reconstructions and have difficulty scaling up to large scenes.
• 2 These limitations are mainly due to their simple fully-connected network architecture that does not incorporate local information in the observations.
• 3 In this paper, we present NICE-SLAM, a dense SLAM system that incorporates multi-level local information by introducing a hierarchical scene representation.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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