← Back to all news
arXiv Computer Vision By Younghyun Noh, Minje Kim, Tae-Kyun Kim

Sparse-GS2Mesh: 3D Gaussian Splatting Guided by Novel Stereo Views and 2DGS for Sparse View Surface Reconstruction

Read the original on arXiv Computer Vision →

Sparse-GS2Mesh is a stereo-aware framework that improves 3D surface reconstruction from sparse camera views by combining 3D Gaussian Splatting (3DGS) with stereo matching techniques. The method begins by initializing 3DGS with epipolar depth priors to reduce overfitting, then applies adaptive baseline selection, stereo-matching‑guided fine‑tuning, and 2D/3D co‑regularization to refine the model. Experiments show a 15% performance gain over state‑of‑the‑art methods in low‑overlap scenarios and comparable results in high‑overlap settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

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.

More like this →
Hugging Face Trending Papers
Jun 29

StereoGS: Sparse-View 3D Gaussian Splatting via Stereo Priors

3D Gaussian Splatting (3DGS) has achieved remarkable success in real-time novel view synthesis, yet it suffers from severe overfitting under sparse-view settings due to insufficient geometric constraints. While recent methods introduce monocular depth priors to mitigate this, they inherently struggle with scale ambiguity and cross-view inconsistency, leading to defective geometry.

computer-visionbenchmarks
More like this →