ForeSplat introduces MetaGrad, a meta-gradient training rule that makes feed-forward 3DGS predictions optimization-aware so they converge faster and to higher quality upon refinement.
AGG: Amor- tized Generative 3D Gaussians for Single Image to 3D.arXiv preprint 2401.04099
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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.
A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.
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ForeSplat: Optimization-Aware Foresight for Feed-Forward 3D Gaussian Splatting
ForeSplat introduces MetaGrad, a meta-gradient training rule that makes feed-forward 3DGS predictions optimization-aware so they converge faster and to higher quality upon refinement.
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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.