# NID-SLAM: Neural Implicit Representation-based RGB-D SLAM in dynamic environments

arXiv: https://arxiv.org/abs/2401.01189 · HTML: https://mlsanity.com/paper/2401.01189/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Builds on (most cited first, 14 of 14)

- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021)
- [iMAP: Implicit Mapping and Positioning in Real-Time](https://mlsanity.com/paper/2103.12352/) (2021)
- [Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM](https://mlsanity.com/paper/2304.14377/) (2023)
- [NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM](https://mlsanity.com/paper/2302.03594/) (2023)
- [ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields](https://mlsanity.com/paper/2211.11704/) (2022)
- [Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation](https://mlsanity.com/paper/2210.15858/) (2022)
- [DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes](https://mlsanity.com/paper/1806.05620/) (2018)
- [DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments](https://mlsanity.com/paper/1809.08379/) (2018)
- [DI-Fusion: Online Implicit 3D Reconstruction with Deep Priors](https://mlsanity.com/paper/2012.05551/) (2020)
- [H2-Mapping: Real-time Dense Mapping Using Hierarchical Hybrid Representation](https://mlsanity.com/paper/2306.03207/) (2023)
- [CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Probability in Dynamic Environments](https://mlsanity.com/paper/2202.01938/) (2022)
- [MA-NeRF: Motion-Assisted Neural Radiance Fields for Face Synthesis from Sparse Images](https://mlsanity.com/paper/2306.10350/) (2023)

## Built on by (most cited first, 10 of 10)

- [How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey](https://mlsanity.com/paper/2402.13255/) (2024)
- [DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM](https://mlsanity.com/paper/2401.01545/) (2024)
- [DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding](https://mlsanity.com/paper/2403.11776/) (2024)
- [Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments](https://mlsanity.com/paper/2506.05965/) (2025)
- [Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAM](https://mlsanity.com/paper/2504.04844/) (2025)
- [Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions](https://mlsanity.com/paper/2602.04251/) (2026)
- [CAD-SLAM: Consistency-Aware Dynamic SLAM with Dynamic-Static Decoupled Mapping](https://mlsanity.com/paper/2505.19420/) (2025)
- [ProDyG: Progressive Dynamic Scene Reconstruction via Gaussian Splatting from Monocular Videos](https://mlsanity.com/paper/2509.17864/) (2025)
- [ACE-SLAM: Scene Coordinate Regression for Neural Implicit Real-Time SLAM](https://mlsanity.com/paper/2512.14032/) (2025)
- [DL-SLAM: Enabling High-Fidelity Gaussian Splatting SLAM in Dynamic Environments based on Dual-Level Probability](https://mlsanity.com/paper/2607.01860/) (2026)

## Abstract (arXiv)

Neural implicit representations have been explored to enhance visual SLAM algorithms, especially in providing high-fidelity dense map. Existing methods operate robustly in static scenes but struggle with the disruption caused by moving objects. In this paper we present NID-SLAM, which significantly improves the performance of neural SLAM in dynamic environments. We propose a new approach to enhance inaccurate regions in semantic masks, particularly in marginal areas. Utilizing the geometric information present in depth images, this method enables accurate removal of dynamic objects, thereby reducing the probability of camera drift. Additionally, we introduce a keyframe selection strategy for dynamic scenes, which enhances camera tracking robustness against large-scale objects and improves the efficiency of mapping. Experiments on publicly available RGB-D datasets demonstrate that our method outperforms competitive neural SLAM approaches in tracking accuracy and mapping quality in dynamic environments.