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NICE-SLAM: Neural Implicit Scalable Encoding for SLAM

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arxiv 2112.12130 v2 pith:UW4LFSG3 submitted 2021-12-22 cs.CV

Zihan Zhu , Songyou Peng , Viktor Larsson , Weiwei Xu , Hujun Bao , Zhaopeng Cui , Martin R. Oswald , Marc Pollefeys This is my paper · ORCID
classification cs.CV
keywords nice-slamslamimplicitneuralinformationlargelocalmapping
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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

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Princeton365: A Diverse Dataset with Accurate Camera Pose

    cs.CV 2025-06 conditional novelty 7.0 of 10

    Princeton365 is a 365-video SLAM/NVS benchmark with board-calibrated millimeter-accurate 6-DoF poses, a new scale-aware optical-flow error metric, and an NVS benchmark of fully non-Lambertian 360-degree scans.

  2. Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments

    cs.LG 2026-01 conditional novelty 6.0 of 10

    Flow equivariant world models use a latent memory that shifts with the agent and with inferred object motion, giving stable long-horizon prediction under partial observability.

  3. NSL-SLAM: High-Fidelity Neural Structured-Light Depth for Practical SLAM and Reconstruction

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Injecting frozen monocular depth features into neural structured-light decoding cuts Replica-SL depth RMSE ~35% vs NSL and, with depth-centric GICP+sparse anchors+light BA, yields the most stable real D435 SLAM among ...

  4. LEG-SLAM: Real-Time Language-Enhanced Gaussian Splatting for SLAM

    cs.CV 2025-06 conditional novelty 5.0 of 10

    LEG-SLAM is a real-time RGB-D SLAM that jointly renders photorealistic images and open-vocabulary semantic masks by distilling PCA-compressed DINOv2 features into 3D Gaussians.

  5. Embodied Spatial Intelligence: from Implicit Scene Modeling to Spatial Reasoning

    cs.RO 2025-08 conditional novelty 4.0 of 10

    The thesis demonstrates that combining implicit 3D scene representations with LLM-based reasoning, using text as an interface, yields strong performance on robotic perception and spatial language tasks.

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