arXiv Computer Vision
TopoSurfel: Closing the Loop between Gaussian Surfels and Meshes for Surface Reconstruction
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Aug 11
Gaussian Sculpting: End-to-End Controllable Surface Reconstruction via Field Optimization
3D Gaussian Splatting (3DGS) has recently enabled real-time novel view synthesis with impressive quality. However, it struggles to recover accurate surfaces under limited viewpoints and due to the inherent irregularity of Gaussian primitives.
arXiv Computer Vision
Sep 1
Gaussian-Voxel Duet: A Dual-Scaffolding Hybrid Representation for Fast and Accurate Monocular Surface Reconstruction
arXiv:2605.26616v2 Announce Type: replace Abstract: While 3D Gaussian Splatting has achieved remarkable success in photorealistic novel view synthesis, its pursuit of fast and high-fidelity 3D recons...
Hugging Face Trending Papers
Aug 19
CoMVS-GS: Collaborative Multi-View Stereo and 3D Gaussian Splatting for Surface Reconstruction
CoMVS‑GS is a surface‑reconstruction framework that fuses Multi‑View Stereo (MVS) with 3D Gaussian splatting. It initializes Gaussian primitives from dense MVS points, uses PatchMatch‑3DGS mutual supervision to refine depths and normals, and replaces voxel‑based meshing with a Delaunay graph‑cut pipeline. Experiments on DTU, GauU‑Scene V2, and MatrixCity demonstrate competitive object‑level results and improved geometric accuracy and mesh compactness in outdoor scenes while preserving high rendering quality.
arXiv Computer Vision
Oct 2
MEGA: Object-Level Mesh Extraction from 3D Gaussian Splatting via Spatial Visual Distillation
MEGA is a new framework that extracts object-level, watertight meshes from 3D Gaussian Splatting (3DGS) scenes. It uses a segment-then-mesh approach, leveraging Spatial Visual Distillation (SVD) to sample diverse camera views of each segmented object and train a mesh reconstruction model with photometric supervision. Experiments on popular benchmarks show that MEGA outperforms existing methods in accurately recovering object-level 3D occupancy and supports complex physical interactions by combining high-quality meshes with photorealistic 3DGS rendering.
arXiv Computer Vision
Sep 3
InceptionGS: Generative Bootstrapping for Large-Scale Gaussian Splatting under Unstructured View Sampling
InceptionGS is a method that improves large‑scale Gaussian splatting for scenes captured with unstructured view sampling. It starts from an initial Gaussian splatting and selectively repairs areas affected by sparse views by integrating scene‑ and view‑adaptive generative priors, while keeping well‑covered regions unchanged. Experiments on real‑world scenes show that this hybrid reconstruction‑generation approach yields higher‑fidelity results than existing methods.
arXiv Computer Vision
Sep 22
LiteTex-GS: Fast and Lightweight Texturing for Gaussian Splatting
arXiv:2609.23380v1 Announce Type: new Abstract: Gaussian Splatting has enabled real-time novel view synthesis, but its tightly coupled geometry and appearance representation often require a large num...