Can 3D networks learn the true shape of surfaces?

Most 3D learning models focus on external coordinates or semantic labels, missing the underlying geometry of shapes themselves. PRISM instead learns by reconstructing geodesic distances—the true shortest paths on a surface—using a topology constraint and two-stage training to handle imbalanced distance distributions. It outperforms baselines on shape recognition, surface mapping, and non-rigid matching. Code released.