AI RESEARCH
ForeSplat: Optimization-Aware Foresight for Feed-Forward 3D Gaussian Splatting
arXiv CS.CV
•
ArXi:2605.22020v1 Announce Type: new Feed-forward 3D Gaussian Splatting (3DGS) models offer fast single-pass reconstruction,but scaling them to match per-scene optimization quality is fundamentally hindered by the scarcity of large-scale 3D annotations. A practical compromise is predict-then-refine,where post-prediction optimization compensates for the limited capacity of the feed-forward network. However,standard feed-forward 3DGS is trained solely for zero-step rendering error,ignoring whether its output constitutes a good initialization for the downstream optimizer.