title: AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views
summary: 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/
paper_authors._id: 6839217e95fedc63bb4ae475
paper_authors.name: Lihan Jiang
paper_authors._id: 6839217e95fedc63bb4ae476
paper_authors.name: Yucheng Mao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:53:34.206Z
paper_authors._id: 65de9c6cf68c3d3bac330509
paper_authors.avatarUrl: /avatars/150858545ea1a9e7c96d6f227093ac54.svg
paper_authors.fullname: Yucheng Mao
paper_authors.type: user
paper_authors.user: matthewmao
paper_authors._id: 6839217e95fedc63bb4ae477
paper_authors.name: Linning Xu
paper_authors._id: 6839217e95fedc63bb4ae478
paper_authors.name: Tao Lu
paper_authors._id: 6839217e95fedc63bb4ae479
paper_authors.name: Kerui Ren
paper_authors._id: 6839217e95fedc63bb4ae47a
paper_authors.name: Yichen Jin
paper_authors._id: 6839217e95fedc63bb4ae47b
paper_authors.name: Xudong Xu
paper_authors._id: 6839217e95fedc63bb4ae47c
paper_authors.name: Mulin Yu
paper_authors._id: 6839217e95fedc63bb4ae47d
paper_authors.name: Jiangmiao Pang
paper_authors._id: 6839217e95fedc63bb4ae47e
paper_authors.name: Feng Zhao
paper_authors._id: 6839217e95fedc63bb4ae47f
paper_authors.name: Dahua Lin
paper_authors._id: 6839217e95fedc63bb4ae480
paper_authors.name: Bo Dai
paper_title: AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views
paper_summary: 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/
paper_ai_summary: 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.
paper_ai_keywords: 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