AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views
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/
6839217e95fedc63bb4ae475
Lihan Jiang
6839217e95fedc63bb4ae476
Yucheng Mao
claimed_verified
2025-05-30T06:53:34.206Z
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/avatars/150858545ea1a9e7c96d6f227093ac54.svg
Yucheng Mao
user
matthewmao
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Linning Xu
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Tao Lu
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Kerui Ren
6839217e95fedc63bb4ae47a
Yichen Jin
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Xudong Xu
6839217e95fedc63bb4ae47c
Mulin Yu
6839217e95fedc63bb4ae47d
Jiangmiao Pang
6839217e95fedc63bb4ae47e
Feng Zhao
6839217e95fedc63bb4ae47f
Dahua Lin
6839217e95fedc63bb4ae480
Bo Dai
AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views
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/
AnySplat is a feed forward network that performs novel view synthesis without camera poses, using 3D Gaussian primitives and unified design for efficiency and quality across sparse and dense datasets.
feed forward network novel view synthesis uncalibrated image collections 3D Gaussian primitives camera intrinsics camera extrinsics neural rendering pipelines per scene optimization zero shot evaluations pose aware baselines pose free approaches rendering latency optimization based neural fields