mlsanityData to 7 Oct 2026

Incremental Online Scene Reconstruction by 3D Gaussian Triangulation

arXiv 2607.10690 ↗First posted 12 Jul 2026cs.CVcited by 0

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Abstract (arXiv)

Incremental scene reconstruction is essential for real-world applications. Although 3D Gaussian Splatting shows strong potential, most existing approaches require offline conversion of the optimized Gaussians into an intermediate implicit field for explicit mesh extraction, which hinders seamless integration with downstream tasks. To address this limitation, we propose a novel online framework that incrementally reconstructs and updates high-fidelity explicit meshes by directly triangulating a dense geometric Gaussian representation, which supports both high-quality rendering and incremental surface reconstruction. Moreover, we present a direct meshing algorithm that efficiently extracts and updates the mesh from the Gaussian set. To ensure mesh accuracy, we enforce a plane-based pulling constraint that dynamically aligns 3D Gaussian primitives to the approximated local surface. Furthermore, our framework significantly reduces memory and computational overhead during long-sequence processing by dynamically freezing fully optimized historical regions. Experiments on public datasets demonstrate that our method outperforms conventional Gaussian-based methods on both rendering quality and reconstruction accuracy.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 29 references that are in the map. First posted on arXiv 12 Jul 2026.

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19 of 29 references.

References · 19

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
  3. 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,061
  4. 2019The Replica Dataset: A Digital Replica of Indoor Spacescited by 885
  5. 2023ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenescited by 747
  6. 2023Scaffold-GS: Structured 3D Gaussians for View-Adaptive Renderingcited by 684
  7. 2023Mip-Splatting: Alias-free 3D Gaussian Splattingcited by 661
  8. 2023SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Renderingcited by 611
  9. 2021NICE-SLAM: Neural Implicit Scalable Encoding for SLAMcited by 586
  10. 2021iMAP: Implicit Mapping and Positioning in Real-Timecited by 476
  11. 2023Gaussian Splatting SLAMcited by 410
  12. 2023GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfacescited by 249
7 more
  1. 2024Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenescited by 226
  2. 2024GaussianPro: 3D Gaussian Splatting with Progressive Propagationcited by 199
  3. 2023Point-SLAM: Dense Neural Point Cloud-based SLAMcited by 160
  4. 2024High-quality Surface Reconstruction using Gaussian Surfelscited by 159
  5. 2024GaMeS: Mesh-Based Adapting and Modification of Gaussian Splattingcited by 69
  6. 2024Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Setcited by 47
  7. 20173D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detectioncited by 35

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