FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D
  Reconstruction
Authors: name: Jiale Xu, name: Shenghua Gao, name: Ying Shan

Existing sparse-view reconstruction models heavily rely on accurate known
camera poses. However, deriving camera extrinsics and intrinsics from
sparse-view images presents significant challenges. In this work, we present
FreeSplatter, a highly scalable, feed-forward reconstruction framework capable
of generating high-quality 3D Gaussians from uncalibrated sparse-view images
and recovering their camera parameters in mere seconds. FreeSplatter is built
upon a streamlined transformer architecture, comprising sequential
self-attention blocks that facilitate information exchange among multi-view
image tokens and decode them into pixel-wise 3D Gaussian primitives. The
predicted Gaussian primitives are situated in a unified reference frame,
allowing for high-fidelity 3D modeling and instant camera parameter estimation
using off-the-shelf solvers. To cater to both object-centric and scene-level
reconstruction, we train two model variants of FreeSplatter on extensive
datasets. In both scenarios, FreeSplatter outperforms state-of-the-art
baselines in terms of reconstruction quality and pose estimation accuracy.
Furthermore, we showcase FreeSplatter's potential in enhancing the productivity
of downstream applications, such as text/image-to-3D content creation.