Hugging Face Trending Papers
PCAsplat: Gaussian Splatting with Local PCA Regularization
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arXiv Computer Vision
Oct 9
PCAsplat: Gaussian Splatting with Local PCA Regularization
PCAsplat introduces a geometry‑aware regularization framework for Gaussian splatting that uses differentiable local PCA to update Gaussians even when they are occluded or contribute little to sampled camera rays. By regularizing PCA eigenvalues and aligning Gaussian normals with neighborhood normals, the method encourages Gaussians to move onto the underlying surface with isotropic tangent‑plane coverage, thereby reducing floaters. Experiments on DTU, Tanks and Temples, and NeRF Synthetic demonstrate that PCAsplat produces surface‑aligned splats that improve geometry‑processing tasks such as point cloud segmentation and Poisson reconstruction while remaining competitive in novel view synthesis and mesh extraction.
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
Sep 4
STARS-GS: Structure-Aware Regularized Gaussian Splatting for Large-Scale Aerial Surface Reconstruction
STARS-GS is a new structure‑aware 3D Gaussian Splatting framework designed for large‑scale aerial surface reconstruction. It introduces a scene partitioning strategy that preserves continuous scene elements, a neighborhood‑aware Gaussian organization that extends geometric constraints to local neighborhoods, and an adaptive surface regularization that tailors regularization strength to local geometry. Experiments on aerial photogrammetry benchmarks show that STARS‑GS improves the average F1‑score from 0.640 to 0.698, a relative gain of about 9.1%.
arXiv Computer Vision
Oct 28
GeoGSplat: Geometry-Grounded Non-Pixel-Aligned Gaussian Splatting
GeoGSplat introduces a geometry‑grounded, feed‑forward Gaussian splatting framework that avoids the pixel‑aligned prediction bottleneck by using a fixed number of Gaussians. A dual‑branch encoder builds coarse 3D anchors and fine triplane features, while a hierarchical decoder progressively generates dense 3D points and Gaussian primitives for reconstruction and novel view synthesis. The method outperforms state‑of‑the‑art pixel‑aligned and non‑pixel‑aligned splatting techniques on DTU and NRGBD datasets, especially when rendering views far from the input cameras.
arXiv Computer Vision
Oct 1
FrontierGS: Progressive View-Space Frontier Expansion for Sparse 3D Gaussian Splatting
arXiv:2511.16030v3 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) enables efficient, high-fidelity novel view synthesis, yet its performance degrades severely under sparse-view supervi...
arXiv Computer Vision
Oct 8
The Role of Initialization in 3D Gaussian Splatting
arXiv:2603.20714v4 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has become the method of choice for photo-realistic novel view synthesis (NVS), due to its efficiency and compelling v...