AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views
Authors: name: Lihan Jiang, name: Yucheng Mao, name: Linning Xu, name: Tao Lu, name: Kerui Ren, name: Yichen Jin, name: Xudong Xu, name: Mulin Yu, name: Jiangmiao Pang, name: Feng Zhao, name: Dahua Lin, name: Bo Dai

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
reduce rendering latency compared to optimization based neural fields, bringing
real time novel view synthesis within reach for unconstrained capture
settings.Project page: https://city-super.github.io/anysplat/