Adaptive 3D Reconstruction via Diffusion Priors and \\Forward Curvature-Matching Likelihood Updates
Seunghyeok Shin, Dabin Kim, Hongki Lim
Reconstructing high-quality point clouds from images remains challenging in computer vision. Existing generative model, particularly diffusion model, based approaches that directly learn the posterior may suffer from inflexibility—they require conditioning signals during training, support only fixed numbers of input views, and need complete retraining for different measurements. Recent diffusion-based methods have attempted to address this by combining prior models with likelihood updates, but they rely on heuristic fixed step sizes for likelihood update that lead to slow convergence and suboptimal reconstruction quality. We advance this line of approach by integrating our novel Forward Curvature-Matching(FCM) update method with diffusion sampling. Our method dynamically determines optimal step sizes using only forward automatic differentiation and finite-difference curvature estimates, enabling precise optimization of the likelihood update. This formulation enables high-fidelity reconstruction from both single and multi-view inputs, and supports various input modalities through simple operator substitution—all without retraining. Experiments on ShapeNet and CO3D datasets demonstrate that our method achieves superior reconstruction quality with 10.2\% and 12.8\% improvement in F-score, respectively, validating its efficiency and adaptability for practical applications.