Recent progress in 3D reconstruction has been driven by generative models, moving from traditional multi-view dependence to single-image diffusion model based techniques. However, these innovative approaches often face challenges with sparse view scenarios, requiring known poses or template shapes, often failing in high-resolution reconstructions. Addressing these issues, we introduce the ''F2F'' (Few to Full) framework, designed for crafting high-resolution 3D models from few views and unknown camera poses, creating fully realistic 3D objects without external constraints. F2F employs a hybrid approach, optimizing both implicit and explicit representations through a unique pipeline involving a pretrained diffusion model for pose estimation, a deformable tetrahedra grid for feature volume construction, and an MLP (neural network) for surface optimization. Our method sets a new standard by ensuring surface geometry, topology, and semantic consistency through differentiable rendering, aiming for a comprehensive solution in 3D reconstruction from sparse views.