Pseudo-View Enhancement via Confidence Fusion for Unposed Sparse-View Reconstruction
This addresses the challenging problem of outdoor 3D reconstruction with limited viewpoints, which is important for practical applications, though it appears incremental as it builds on existing diffusion and Gaussian methods.
The paper tackles 3D scene reconstruction from unposed sparse viewpoints in outdoor scenes by proposing a framework with bidirectional pseudo frame restoration and scene perception Gaussian management, achieving substantial gains in fidelity and stability over existing methods on outdoor benchmarks.
3D scene reconstruction under unposed sparse viewpoints is a highly challenging yet practically important problem, especially in outdoor scenes due to complex lighting and scale variation. With extremely limited input views, directly utilizing diffusion model to synthesize pseudo frames will introduce unreasonable geometry, which will harm the final reconstruction quality. To address these issues, we propose a novel framework for sparse-view outdoor reconstruction that achieves high-quality results through bidirectional pseudo frame restoration and scene perception Gaussian management. Specifically, we introduce a bidirectional pseudo frame restoration method that restores missing content by diffusion-based synthesis guided by adjacent frames with a lightweight pseudo-view deblur model and confidence mask inference algorithm. Then we propose a scene perception Gaussian management strategy that optimize Gaussians based on joint depth-density information. These designs significantly enhance reconstruction completeness, suppress floating artifacts and improve overall geometric consistency under extreme view sparsity. Experiments on outdoor benchmarks demonstrate substantial gains over existing methods in both fidelity and stability.