Generalizing Shape-from-Template to Topological Changes
This addresses a limitation in computer vision for applications like medical imaging or robotics where objects undergo topological changes, representing an incremental but practical extension to existing methods.
The paper tackles the problem of reconstructing deformable object surfaces from 3D template to 2D image correspondences when topological changes like tears or cuts occur, proposing an extension to Shape-from-Template that robustly handles such events and consistently outperforms baseline methods in experiments.
Reconstructing the surfaces of deformable objects from correspondences between a 3D template and a 2D image is well studied under Shape-from-Template (SfT) methods; however, existing approaches break down when topological changes accompany the deformation. We propose a principled extension of SfT that enables reconstruction in the presence of such changes. Our approach is initialized with a classical SfT solution and iteratively adapts the template by partitioning its spatial domain so as to minimize an energy functional that jointly encodes physical plausibility and reprojection consistency. We demonstrate that the method robustly captures a wide range of practically relevant topological events including tears and cuts on bounded 2D surfaces, thereby establishing the first general framework for topological-change-aware SfT. Experiments on both synthetic and real data confirm that our approach consistently outperforms baseline methods.