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:
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本文介绍了一个名为 NICE-SLAM 的密集 SLAM 系统，它通过引入分层场景表示来结合多层次的局部信息，并通过预训练的几何先验来优化该表示，从而在大型室内场景中实现了细节重建。与最近的神经隐式 SLAM 系统相比，我们的方法更具可扩展性、高效性和鲁棒性。