Applications
WildSplatter: Feed-forward 3D Gaussian Splatting with Appearance Control from Unconstrained Images
arXiv:2604.21182v1 Announce Type: new Abstract: We propose WildSplatter, a feed-forward 3D Gaussian Splatting (3DGS) model for unconstrained images with unknown camera parameters and varying lighting
arXiv:2604.21182v1 Announce Type: new Abstract: We propose WildSplatter, a feed-forward 3D Gaussian Splatting (3DGS) model for unconstrained images with unknown camera parameters and varying lighting conditions. 3DGS is an effective scene representation that enables high-quality, real-time rendering; however, it typically requires iterative optimization and multi-view images captured under consistent lighting with known camera parameters. WildSplatter is trained on unconstrained photo collections and jointly learns 3D Gaussians and appearance embeddings conditioned on input images. This design enables flexible modulation of Gaussian colors to represent significant variations in lighting and appearance. Our method reconstructs 3D Gaussians from sparse input views in under one second, while also enabling appearance control under diverse lighting conditions. Experimental results demonstrate that our approach outperforms existing pose-free 3DGS methods on challenging real-world datasets with varying illumination.
Related
- LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning
- Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective
- MSGS: Multispectral 3D Gaussian Splatting
- PDF-GS: Progressive Distractor Filtering for Robust 3D Gaussian Splatting
- ArtifactWorld: Scaling 3D Gaussian Splatting Artifact Restoration via Video Generation Models
Source: arXiv cs.CV | 2026-04-24