Poisson Surface Reconstruction

Poisson surface reconstruction is a computational technique used to create a 3D surface model from a set of scattered 3D points, often obtained from scanning processes. Current research focuses on improving the accuracy, efficiency, and robustness of reconstruction algorithms, particularly addressing challenges like noisy data, varying point densities, and complex geometries. This involves developing novel neural network architectures, such as those based on Fourier Neural Operators or coupled Lie-Poisson networks, and incorporating techniques like gradient-domain optimization and Poisson blending to enhance detail and smoothness. The resulting improvements have significant implications for various fields, including medical imaging, computer graphics, and robotics, enabling more accurate and efficient 3D model generation from point cloud data.