# GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM

arXiv: https://arxiv.org/abs/2403.19549 · HTML: https://mlsanity.com/paper/2403.19549/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 56 of its 91 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 30 of 56)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [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)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [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)
- [Gaussian Splatting SLAM](https://mlsanity.com/paper/2312.06741/) (2023)
- [SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM](https://mlsanity.com/paper/2312.02126/) (2023)
- [Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans](https://mlsanity.com/paper/2110.04994/) (2021)
- [Neural RGB-D Surface Reconstruction](https://mlsanity.com/paper/2104.04532/) (2021)
- [GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting](https://mlsanity.com/paper/2311.11700/) (2023)
- [NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video](https://mlsanity.com/paper/2104.00681/) (2021)
- [NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields](https://mlsanity.com/paper/2210.13641/) (2022)
- [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)
- [Point-SLAM: Dense Neural Point Cloud-based SLAM](https://mlsanity.com/paper/2304.04278/) (2023)
- [ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields](https://mlsanity.com/paper/2211.11704/) (2022)
- [Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting](https://mlsanity.com/paper/2312.10070/) (2023)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation](https://mlsanity.com/paper/2210.15858/) (2022)
- [Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2311.16728/) (2023)
- [GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction](https://mlsanity.com/paper/2309.02436/) (2023)
- [iSDF: Real-Time Neural Signed Distance Fields for Robot Perception](https://mlsanity.com/paper/2204.02296/) (2022)
- [TransformerFusion: Monocular RGB Scene Reconstruction using Transformers](https://mlsanity.com/paper/2107.02191/) (2021)

## Built on by (most cited first, 30 of 36)

- [MUSt3R: Multi-view Network for Stereo 3D Reconstruction](https://mlsanity.com/paper/2503.01661/) (2025)
- [SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos](https://mlsanity.com/paper/2412.09401/) (2024)
- [Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians](https://mlsanity.com/paper/2405.16544/) (2024)
- [LoopSplat: Loop Closure by Registering 3D Gaussian Splats](https://mlsanity.com/paper/2408.10154/) (2024)
- [WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments](https://mlsanity.com/paper/2504.03886/) (2025)
- [TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation](https://mlsanity.com/paper/2505.10696/) (2025)
- [HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction](https://mlsanity.com/paper/2411.17982/) (2024)
- [Outdoor Monocular SLAM with Global Scale-Consistent 3D Gaussian Pointmaps](https://mlsanity.com/paper/2507.03737/) (2025)
- [AMB3R: Accurate Feed-forward Metric-scale 3D Reconstruction with Backend](https://mlsanity.com/paper/2511.20343/) (2025)
- [RGB-Only Gaussian Splatting SLAM for Unbounded Outdoor Scenes](https://mlsanity.com/paper/2502.15633/) (2025)
- [NeRFs in Robotics: A Survey](https://mlsanity.com/paper/2405.01333/) (2024)
- [ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association](https://mlsanity.com/paper/2509.01584/) (2025)
- [DROID-Splat: Combining end-to-end SLAM with 3D Gaussian Splatting](https://mlsanity.com/paper/2411.17660/) (2024)
- [Reconstructing 4D Spatial Intelligence: A Survey](https://mlsanity.com/paper/2507.21045/) (2025)
- [VPGS-SLAM: Voxel-based Progressive 3D Gaussian SLAM in Large-Scale Scenes](https://mlsanity.com/paper/2505.18992/) (2025)
- [PINGS: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural Map](https://mlsanity.com/paper/2502.05752/) (2025)
- [Can Video Diffusion Model Reconstruct 4D Geometry?](https://mlsanity.com/paper/2503.21082/) (2025)
- [ODHSR: Online Dense 3D Reconstruction of Humans and Scenes from Monocular Videos](https://mlsanity.com/paper/2504.13167/) (2025)
- [SplatMAP: Online Dense Monocular SLAM with 3D Gaussian Splatting](https://mlsanity.com/paper/2501.07015/) (2025)
- [IG-SLAM: Instant Gaussian SLAM](https://mlsanity.com/paper/2408.01126/) (2024)
- [Splat-LOAM: Gaussian Splatting LiDAR Odometry and Mapping](https://mlsanity.com/paper/2503.17491/) (2025)
- [MVS-GS: High-Quality 3D Gaussian Splatting Mapping via Online Multi-View Stereo](https://mlsanity.com/paper/2412.19130/) (2024)
- [Deblur Gaussian Splatting SLAM](https://mlsanity.com/paper/2503.12572/) (2025)
- [MCGS-SLAM: A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping](https://mlsanity.com/paper/2509.14191/) (2025)
- [Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions](https://mlsanity.com/paper/2602.04251/) (2026)
- [From Fields to Splats: A Cross-Domain Survey of Real-Time Neural Scene Representations](https://mlsanity.com/paper/2509.23555/) (2025)
- [RGS-SLAM: Robust Gaussian Splatting SLAM with One-Shot Dense Initialization](https://mlsanity.com/paper/2601.00705/) (2025)
- [WildPose: A Unified Framework for Robust Pose Estimation in the Wild](https://mlsanity.com/paper/2605.12774/) (2026)
- [UniSim-SLAM: Feed-Forward SLAM with Unified Sim(3) Optimization](https://mlsanity.com/paper/2608.01706/) (2026)
- [An Earth Rover dataset recorded at the ICRA@40 party](https://mlsanity.com/paper/2407.05735/) (2024)

## Abstract (arXiv)

Recent advancements in RGB-only dense Simultaneous Localization and Mapping (SLAM) have predominantly utilized grid-based neural implicit encodings and/or struggle to efficiently realize global map and pose consistency. To this end, we propose an efficient RGB-only dense SLAM system using a flexible neural point cloud scene representation that adapts to keyframe poses and depth updates, without needing costly backpropagation. Another critical challenge of RGB-only SLAM is the lack of geometric priors. To alleviate this issue, with the aid of a monocular depth estimator, we introduce a novel DSPO layer for bundle adjustment which optimizes the pose and depth of keyframes along with the scale of the monocular depth. Finally, our system benefits from loop closure and online global bundle adjustment and performs either better or competitive to existing dense neural RGB SLAM methods in tracking, mapping and rendering accuracy on the Replica, TUM-RGBD and ScanNet datasets. The source code is available at https://github.com/zhangganlin/GlOIRE-SLAM