We introduce SPFSplatV2, an efficient feed-forward framework for 3D Gaussian splatting from sparse multi-view images, requiring no ground-truth poses during training and inference. It employs a shared feature extraction backbone, enabling simultaneous prediction of 3D Gaussian primitives and camera poses in a canonical space from unposed inputs. A masked attention mechanism is introduced to efficiently estimate target poses during training, while a reprojection loss enforces pixel-aligned Gaussian primitives, providing stronger geometric constraints. We further demonstrate the compatibility of our training framework with different reconstruction architectures, resulting in two model variants. Remarkably, despite the absence of pose supervision, our method achieves state-of-the-art performance in both in-domain and out-of-domain novel view synthesis, even under extreme viewpoint changes and limited image overlap, and surpasses recent methods that rely on geometric supervision for relative pose estimation. By eliminating dependence on ground-truth poses, our method offers the scalability to leverage larger and more diverse datasets.
SPFSplatV2 employs a shared backbone with three specialized heads simultaneously predicts Gaussian centers, additional Gaussian parameters, and camera poses from unposed images in a canonical space, with the first input view as the reference. Encoder tokens, concatenated with a learnable pose token and an optional embedding of ground-truth intrinsics, are fed into the decoder, which employs masked attention to prevent context tokens from attending to target tokens, ensuring Gaussian reconstruction remains independent of target-view information. The 3D Gaussians are optimized via a rendering loss using the predicted target poses, while a reprojection loss enforces alignment between Gaussian centers and their corresponding pixels using the predicted context poses. By jointly optimizing Gaussians and camera poses, the pipeline enhances geometric consistency and improves reconstruction quality.
@article{huang2025spfsplat,
title={SPFSplatV2: Efficient Self-Supervised Pose-Free 3D Gaussian Splatting from Sparse Views},
author={Huang, Ranran and Mikolajczyk, Krystian},
journal={arXiv preprint arXiv:2509.17246},
year={2025}
}