AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views

Yucheng Mao

Abstract:

We introduce AnySplat, a feed-forward network for novel-view synthesis
from uncalibrated image collections. In contrast to traditional neural-rendering
pipelines that demand known camera poses and per-scene optimization, or
recent feed-forward methods that buckle under the computational weight of
dense views—our model predicts everything in one shot. A single forward
pass yields a set of 3D Gaussian primitives encoding both scene geometry
and appearance, and the corresponding camera intrinsics and extrinsics
for each input image. This unified design scales effortlessly to casually
captured, multi-view datasets without any pose annotations. In extensive
zero-shot evaluations, AnySplat matches the quality of pose-aware baselines in both sparse- and dense-view scenarios while surpassing existing
pose-free approaches. Moreover, it greatly reduces rendering latency compared to optimization-based neural fields, bringing real-time novel-view
synthesis within reach for unconstrained capture settings