TriGS: Tri-consistency 3D Gaussian Splatting from Sparse and Unposed Views

Recent advances in 3D scene representation, particularly 3D Gaussian Splatting (3DGS), have demonstrated remarkable photorealistic rendering capabilities. However, the heavy reliance on dense and precisely calibrated camera configurations limits effectiveness in sparse view and unposed scenarios. In this paper, we present Tri-consistency 3D Gaussian Splatting (dubbed TriGS), a novel framework that jointly optimizes 3DGS parameters and camera poses only from sparse and unposed images via triple consistency supervisions coupled with the adaptive regularization strategy. We first estimate coarse camera poses by exploiting 3DGS's anisotropic properties through iterative relative pose optimization. Building upon this foundation, we introduce cross-view consistency enforcement through synchronized photometric color, geometric structure, and deep feature, effectively resolving rendering ambiguities with auxiliary supervisions. A unified rendering paradigm is also proposed to jointly refine Gaussian primitives and camera poses by transforming positions, covariances, and spherical harmonics. To combat overfitting inherent in joint optimization, we devise an adaptive regularization mechanism that strategically samples hard viewpoints based on baseline distances and training dynamics, enforcing projection consistency through deep feature priors. Extensive experiments on multiple challenging real-world datasets validate the effectiveness of TriGS, which achieves satisfactory results to set a new state-of-the-art without the reliance on external pose priors only under sparse and unposed view inputs.