Landscape-Awareness for Geometric View Diffusion Model

Accuracy camera viewpoint estimation under sparse-view conditions remains challenging, particularly in two-view scenarios. Recent approaches leverage diffusion models such as Zero123, which synthesize novel views conditioned on relative viewpoint, and have demonstrated promising performance when repurposed for viewpoint estimation via optimization with MSE loss. However, existing methods often suffer from non-convex loss landscape with numerous local minima, which makes them sensitive to initialization and reliant on na\"ive multi-start strategies to achieve reasonable results. We analyze these optimization challenges and visualize failure cases, showing that ambiguities in object geometry, such as symmetry and self-similarity, can mislead gradient-based updates toward incorrect viewpoints. To address these limitations, we propose a score-based method that reshapes the optimization landscape to guide updates toward the ground-truth viewpoint, followed by a refinement stage using a viewpoint-conditioned diffusion model. Experiments show that our method improves convergence, reduces reliance on brute-force sampling, and achieves competitive accuracy with higher sample-efficiency.