# SNI-SLAM: Semantic Neural Implicit SLAM

arXiv: https://arxiv.org/abs/2311.11016 · HTML: https://mlsanity.com/paper/2311.11016/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2023.

## Built on by (most cited first, 30 of 65)

- [SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM](https://mlsanity.com/paper/2402.03246/) (2024)
- [How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey](https://mlsanity.com/paper/2402.13255/) (2024)
- [PLGSLAM: Progressive Neural Scene Represenation with Local to Global Bundle Adjustment](https://mlsanity.com/paper/2312.09866/) (2023)
- [WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments](https://mlsanity.com/paper/2504.03886/) (2025)
- [SemGauss-SLAM: Dense Semantic Gaussian Splatting SLAM](https://mlsanity.com/paper/2403.07494/) (2024)
- [GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM](https://mlsanity.com/paper/2403.19549/) (2024)
- [What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models](https://mlsanity.com/paper/2512.03422/) (2025)
- [Compact 3D Gaussian Splatting For Dense Visual SLAM](https://mlsanity.com/paper/2403.11247/) (2024)
- [HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction](https://mlsanity.com/paper/2411.17982/) (2024)
- [Neural Radiance Field in Autonomous Driving: A Survey](https://mlsanity.com/paper/2404.13816/) (2024)
- [NIS-SLAM: Neural Implicit Semantic RGB-D SLAM for 3D Consistent Scene Understanding](https://mlsanity.com/paper/2407.20853/) (2024)
- [NEDS-SLAM: A Neural Explicit Dense Semantic SLAM Framework using 3D Gaussian Splatting](https://mlsanity.com/paper/2403.11679/) (2024)
- [Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes](https://mlsanity.com/paper/2404.06050/) (2024)
- [Hier-SLAM: Scaling-up Semantics in SLAM with a Hierarchically Categorical Gaussian Splatting](https://mlsanity.com/paper/2409.12518/) (2024)
- [VPGS-SLAM: Voxel-based Progressive 3D Gaussian SLAM in Large-Scale Scenes](https://mlsanity.com/paper/2505.18992/) (2025)
- [Open-Vocabulary Online Semantic Mapping for SLAM](https://mlsanity.com/paper/2411.15043/) (2024)
- [OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding](https://mlsanity.com/paper/2503.01646/) (2025)
- [DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Diffusion Model](https://mlsanity.com/paper/2311.17456/) (2023)
- [MAMBA4D: Efficient Long-Sequence Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models](https://mlsanity.com/paper/2405.14338/) (2024)
- [SEGS-SLAM: Structure-enhanced 3D Gaussian Splatting SLAM with Appearance Embedding](https://mlsanity.com/paper/2501.05242/) (2025)
- [MCN-SLAM: Multi-Agent Collaborative Neural SLAM with Hybrid Implicit Neural Scene Representation](https://mlsanity.com/paper/2506.18678/) (2025)
- [Ov3R: Open-Vocabulary Semantic 3D Reconstruction from RGB Videos](https://mlsanity.com/paper/2507.22052/) (2025)
- [Multi-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge](https://mlsanity.com/paper/2405.16464/) (2024)
- [Inpaint360GS: Efficient Object-Aware 3D Inpainting via Gaussian Splatting for 360deg Scenes](https://mlsanity.com/paper/2511.06457/) (2025)
- [Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2311.17491/) (2023)
- [RGBDS-SLAM: A RGB-D Semantic Dense SLAM Based on 3D Multi Level Pyramid Gaussian Splatting](https://mlsanity.com/paper/2412.01217/) (2024)
- [DINO-SLAM: DINO-informed RGB-D SLAM for Neural Implicit and Explicit Representations](https://mlsanity.com/paper/2507.19474/) (2025)
- [GRS-SLAM3R: Real-Time Dense SLAM with Gated Recurrent State](https://mlsanity.com/paper/2509.23737/) (2025)
- [VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM](https://mlsanity.com/paper/2512.02293/) (2025)
- [RoGs: Large Scale Road Surface Reconstruction with Meshgrid Gaussian](https://mlsanity.com/paper/2405.14342/) (2024)

## Abstract (arXiv)

We propose SNI-SLAM, a semantic SLAM system utilizing neural implicit representation, that simultaneously performs accurate semantic mapping, high-quality surface reconstruction, and robust camera tracking. In this system, we introduce hierarchical semantic representation to allow multi-level semantic comprehension for top-down structured semantic mapping of the scene. In addition, to fully utilize the correlation between multiple attributes of the environment, we integrate appearance, geometry and semantic features through cross-attention for feature collaboration. This strategy enables a more multifaceted understanding of the environment, thereby allowing SNI-SLAM to remain robust even when single attribute is defective. Then, we design an internal fusion-based decoder to obtain semantic, RGB, Truncated Signed Distance Field (TSDF) values from multi-level features for accurate decoding. Furthermore, we propose a feature loss to update the scene representation at the feature level. Compared with low-level losses such as RGB loss and depth loss, our feature loss is capable of guiding the network optimization on a higher-level. Our SNI-SLAM method demonstrates superior performance over all recent NeRF-based SLAM methods in terms of mapping and tracking accuracy on Replica and ScanNet datasets, while also showing excellent capabilities in accurate semantic segmentation and real-time semantic mapping.