arXiv Computer Vision
Unlocking Zero-shot Potential of Semi-dense Image Matching via Gaussian Splatting
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
3d ago
Filling the Unseen: Scene Extrapolation via 3D Gaussian Splatting
arXiv:2609.13262v1 Announce Type: new Abstract: 3D Gaussian Splatting achieves photorealistic reconstruction within training view distribution, yet it degrades on out-of-distribution novel views, exh...
arXiv Computer Vision
1d ago
CADSplat: Sparse-View 3D Gaussian Splatting Aided by CAD Models for Robust, Photorealistic Digital-Twin Reconstruction
arXiv:2609.18473v1 Announce Type: new Abstract: We present CADSplat, a framework that reconstructs photorealistic, geometrically accurate digital twins from sparse ($<15$ views), wide-baseline posed...
arXiv AI
Jul 2
Improving Sparse-View 3DGS Generalization via Flat Minima Optimization
arXiv:2607. 00885v1 Announce Type: cross Abstract: Recent advances in neural rendering have established 3D Gaussian Splatting (3DGS) as a highly efficient representation for novel view synthesis, enabling fast training and real-time rendering with strong fidelity.
Hugging Face Trending Papers
Aug 11
CasDeblurGS: Cascaded 2D-to-3D Multi-View Consistency for 3D Gaussian Splatting from Two Blurry Images
Free-viewpoint 3D scene media is increasingly important for immersive applications, yet practical capture often suffers from severe view sparsity and motion blur. Although neural rendering has advanced sparse-view synthesis, existing blur-aware methods typically require substantial multi-view redundancy, accurate camera poses, or costly per-scene optimization.
arXiv Computer Vision
Aug 31
WilLaGS: Latent-Conditional 3D Appearance Fields for Robust Gaussian Splatting In-the-Wild
WilLaGS introduces a unified framework that enhances 3D Gaussian Splatting for in-the-wild scenes by learning a continuous global appearance manifold with a β‑VAE and generating dynamic Tri‑Plane features for spatially‑varying local illumination. It also incorporates a self‑supervised perceptual masking mechanism using a Teacher‑Student EMA architecture to suppress transient artifacts and identify inconsistent regions. Experiments on multiple datasets show that WilLaGS achieves state‑of‑the‑art reconstruction quality and novel view synthesis while preserving real‑time rendering efficiency.
arXiv Computer Vision
Sep 4
Rethinking 3D Noise: Learning 3D-Aware Video Priors via Optimization-Free Morphological Perturbations
The paper introduces 3D Morphological Perturbations, an optimization‑free regularizer for 3D representations such as NeRF and 3D Gaussian Splatting. By treating each Gaussian as a pixel‑like element, the method applies scale, rotation, and pruning perturbations to preserve spatial consistency across views, eliminating the need for per‑scene optimization during dataset curation. Experiments on a lightweight video diffusion sandbox and a 14B‑parameter video model show that the approach improves geometric priors, reduces mean depth error by 12.5% over state‑of‑the‑art 3D artifact refiners, and boosts downstream robotics policy success rates by up to 8.0% on three manipulation tasks.