WildSplat decouples geometry from appearance in a single feedforward pass to produce appearance-conditioned 3D Gaussian reconstructions from unposed in-the-wild images.
Explicit correspondence matching for generalizable neural radiance fields.IEEE Trans
2 Pith papers cite this work, alongside 6 external citations. Polarity classification is still indexing.
2
Pith papers citing it
6
external citations · external index
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2years
2026 2roles
background 1polarities
background 1representative citing papers
The paper proposes a problem-driven taxonomy for feed-forward 3D scene modeling that groups methods by five core challenges: feature enhancement, geometry awareness, model efficiency, augmentation strategies, and temporal-aware modeling.
citing papers explorer
-
WildSplat: Feedforward Gaussian Splatting from Unposed In-the-Wild Images
WildSplat decouples geometry from appearance in a single feedforward pass to produce appearance-conditioned 3D Gaussian reconstructions from unposed in-the-wild images.
-
Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective
The paper proposes a problem-driven taxonomy for feed-forward 3D scene modeling that groups methods by five core challenges: feature enhancement, geometry awareness, model efficiency, augmentation strategies, and temporal-aware modeling.