Abstract
Surface reconstruction from point clouds is a core challenge in computer vision, especially when the data is partial, noisy, cluttered, and unordered. We introduce BaySurf-SANF, a novel end-to-end probabilistic generative framework that reconstructs 3D surfaces from such degraded inputs. Taking a Bayesian approach in a two-step pipeline, it combines Graph Neural Networks and Transformer encoders to extract local features, and shared latent-space shape priors in the form of a conditional normalizing flow, resulting in posterior samples on the surface representation space. To improve expressiveness, we replace widely used affine layers by Affine Dynamic Layers (ADLs), enabling directional data transformations. Additionally, we simultaneously estimate surface normals to ensure geometric consistency between reconstructed surfaces and their normals. Experimental results demonstrate that BaySurf-SANF achieves state-of-the-art performance across datasets with varying noise levels, outperforming existing explicit reconstruction and generative methods. An ablation study highlights the contribution of each module. These results confirm robustness and flexibility. The source code is available at https://github.com/XMa35/BaySurf-SANF.