# Real-time Scalable Dense Surfel Mapping

arXiv: https://arxiv.org/abs/1909.04250 · HTML: https://mlsanity.com/paper/1909.04250/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 20 of 20)

- [Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation](https://mlsanity.com/paper/2404.15506/) (2024)
- [GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting](https://mlsanity.com/paper/2311.11700/) (2023)
- [A Survey on 3D Gaussian Splatting](https://mlsanity.com/paper/2401.03890/) (2024)
- [Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation](https://mlsanity.com/paper/2210.15858/) (2022)
- [MIPS-Fusion: Multi-Implicit-Submaps for Scalable and Robust Online Neural RGB-D Reconstruction](https://mlsanity.com/paper/2308.08741/) (2023)
- [ManhattanSLAM: Robust Planar Tracking and Mapping Leveraging Mixture of Manhattan Frames](https://mlsanity.com/paper/2103.15068/) (2021)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description](https://mlsanity.com/paper/2108.00355/) (2021)
- [GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mapping](https://mlsanity.com/paper/2501.08672/) (2025)
- [A Hybrid Sparse-Dense Monocular SLAM System for Autonomous Driving](https://mlsanity.com/paper/2108.07736/) (2021)
- [Vox-Fusion++: Voxel-based Neural Implicit Dense Tracking and Mapping with Multi-maps](https://mlsanity.com/paper/2403.12536/) (2024)
- [MotionGS : Compact Gaussian Splatting SLAM by Motion Filter](https://mlsanity.com/paper/2405.11129/) (2024)
- [Hi-Map: Hierarchical Factorized Radiance Field for High-Fidelity Monocular Dense Mapping](https://mlsanity.com/paper/2401.03203/) (2024)
- [MemGS: Memory-Efficient Gaussian Splatting for Real-Time SLAM](https://mlsanity.com/paper/2509.13536/) (2025)
- [NeuV-SLAM: Fast Neural Multiresolution Voxel Optimization for RGBD Dense SLAM](https://mlsanity.com/paper/2402.02020/) (2024)
- [GS3LAM: Gaussian Semantic Splatting SLAM](https://mlsanity.com/paper/2603.27781/) (2026)
- [GI-SLAM: Gaussian-Inertial SLAM](https://mlsanity.com/paper/2503.18275/) (2025)
- [Neural Semantic Map-Learning for Autonomous Vehicles](https://mlsanity.com/paper/2410.07780/) (2024)
- [Online Localisation and Colored Mesh Reconstruction Architecture for 3D Visual Feedback in Robotic Exploration Missions](https://mlsanity.com/paper/2207.10489/) (2022)
- [WeaveRL: Weaving Reconstruction into Scene-Aware Fabrics for Perceptive Reinforcement Learning](https://mlsanity.com/paper/2609.18685/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel dense surfel mapping system that scales well in different environments with only CPU computation. Using a sparse SLAM system to estimate camera poses, the proposed mapping system can fuse intensity images and depth images into a globally consistent model. The system is carefully designed so that it can build from room-scale environments to urban-scale environments using depth images from RGB-D cameras, stereo cameras or even a monocular camera. First, superpixels extracted from both intensity and depth images are used to model surfels in the system. superpixel-based surfels make our method both run-time efficient and memory efficient. Second, surfels are further organized according to the pose graph of the SLAM system to achieve O(1) fusion time regardless of the scale of reconstructed models. Third, a fast map deformation using the optimized pose graph enables the map to achieve global consistency in real-time. The proposed surfel mapping system is compared with other state-of-the-art methods on synthetic datasets. The performances of urban-scale and room-scale reconstruction are demonstrated using the KITTI dataset and autonomous aggressive flights, respectively. The code is available for the benefit of the community.