SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian
  Splatting
Authors: name: Gyeongjin Kang, name: Jisang Yoo, name: Jihyeon Park, name: Seungtae Nam, name: Hyeonsoo Im, name: Sangheon Shin, name: Sangpil Kim, name: Eunbyung Park

We propose SelfSplat, a novel 3D Gaussian Splatting model designed to perform
pose-free and 3D prior-free generalizable 3D reconstruction from unposed
multi-view images. These settings are inherently ill-posed due to the lack of
ground-truth data, learned geometric information, and the need to achieve
accurate 3D reconstruction without finetuning, making it difficult for
conventional methods to achieve high-quality results. Our model addresses these
challenges by effectively integrating explicit 3D representations with
self-supervised depth and pose estimation techniques, resulting in reciprocal
improvements in both pose accuracy and 3D reconstruction quality. Furthermore,
we incorporate a matching-aware pose estimation network and a depth refinement
module to enhance geometry consistency across views, ensuring more accurate and
stable 3D reconstructions. To present the performance of our method, we
evaluated it on large-scale real-world datasets, including RealEstate10K, ACID,
and DL3DV. SelfSplat achieves superior results over previous state-of-the-art
methods in both appearance and geometry quality, also demonstrates strong
cross-dataset generalization capabilities. Extensive ablation studies and
analysis also validate the effectiveness of our proposed methods. Code and
pretrained models are available at https://gynjn.github.io/selfsplat/