# SurfelMeshing: Online Surfel-Based Mesh Reconstruction

arXiv: https://arxiv.org/abs/1810.00729 · HTML: https://mlsanity.com/paper/1810.00729/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2018.

## Built on by (most cited first, 29 of 29)

- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [Gaussian Splatting SLAM](https://mlsanity.com/paper/2312.06741/) (2023)
- [FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understanding](https://mlsanity.com/paper/2401.01970/) (2024)
- [Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential Observations](https://mlsanity.com/paper/2108.05851/) (2021)
- [GStex: Per-Primitive Texturing of 2D Gaussian Splatting for Decoupled Appearance and Geometry Modeling](https://mlsanity.com/paper/2409.12954/) (2024)
- [BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion](https://mlsanity.com/paper/2204.01139/) (2022)
- [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)
- [Multi-sensor large-scale dataset for multi-view 3D reconstruction](https://mlsanity.com/paper/2203.06111/) (2022)
- [Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction](https://mlsanity.com/paper/2111.13652/) (2021)
- [ImMesh: An Immediate LiDAR Localization and Meshing Framework](https://mlsanity.com/paper/2301.05206/) (2023)
- [HRBF-Fusion: Accurate 3D reconstruction from RGB-D data using on-the-fly implicits](https://mlsanity.com/paper/2202.01829/) (2022)
- [MotionGS : Compact Gaussian Splatting SLAM by Motion Filter](https://mlsanity.com/paper/2405.11129/) (2024)
- [LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies](https://mlsanity.com/paper/2111.15113/) (2021)
- [LiveNVS: Neural View Synthesis on Live RGB-D Streams](https://mlsanity.com/paper/2311.16668/) (2023)
- [DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding](https://mlsanity.com/paper/2403.11776/) (2024)
- [ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation](https://mlsanity.com/paper/2407.16508/) (2024)
- [Online Learning of a Probabilistic and Adaptive Scene Representation](https://mlsanity.com/paper/2103.16832/) (2021)
- [Smooth Mesh Estimation from Depth Data using Non-Smooth Convex Optimization](https://mlsanity.com/paper/2108.02957/) (2021)
- [Object Pose Estimation Using Implicit Representation For Transparent Objects](https://mlsanity.com/paper/2410.13465/) (2024)
- [CAD-SLAM: Consistency-Aware Dynamic SLAM with Dynamic-Static Decoupled Mapping](https://mlsanity.com/paper/2505.19420/) (2025)
- [AiSDF: Structure-aware Neural Signed Distance Fields in Indoor Scenes](https://mlsanity.com/paper/2403.01861/) (2024)
- [HVOFusion: Incremental Mesh Reconstruction Using Hybrid Voxel Octree](https://mlsanity.com/paper/2404.17974/) (2024)
- [Surfel-based 3D Registration with Equivariant SE(3) Features](https://mlsanity.com/paper/2508.20789/) (2025)
- [SPARK: Scalable Real-Time Point Cloud Aggregation with Multi-View Self-Calibration](https://mlsanity.com/paper/2601.08414/) (2026)
- [FUSE-Flow: Scalable Real-Time Multi-View Point Cloud Reconstruction Using Confidence](https://mlsanity.com/paper/2602.01035/) (2026)
- [Structure-to-Image: Zero-Shot Depth Estimation in Colonoscopy via High-Fidelity Sim-to-Real Adaptation](https://mlsanity.com/paper/2602.21740/) (2026)
- [Triangle Splatting SLAM](https://mlsanity.com/paper/2605.31419/) (2026)
- [Splat-based 3D Scene Reconstruction with Extreme Motion-blur](https://mlsanity.com/paper/2607.16926/) (2026)

## Abstract (arXiv)

We address the problem of mesh reconstruction from live RGB-D video, assuming a calibrated camera and poses provided externally (e.g., by a SLAM system). In contrast to most existing approaches, we do not fuse depth measurements in a volume but in a dense surfel cloud. We asynchronously (re)triangulate the smoothed surfels to reconstruct a surface mesh. This novel approach enables to maintain a dense surface representation of the scene during SLAM which can quickly adapt to loop closures. This is possible by deforming the surfel cloud and asynchronously remeshing the surface where necessary. The surfel-based representation also naturally supports strongly varying scan resolution. In particular, it reconstructs colors at the input camera's resolution. Moreover, in contrast to many volumetric approaches, ours can reconstruct thin objects since objects do not need to enclose a volume. We demonstrate our approach in a number of experiments, showing that it produces reconstructions that are competitive with the state-of-the-art, and we discuss its advantages and limitations. The algorithm (excluding loop closure functionality) is available as open source at https://github.com/puzzlepaint/surfelmeshing .