Curvy: A Parametric Cross-section based Surface Reconstruction
This addresses shape reconstruction for computer graphics or 3D modeling, but appears incremental as it builds on existing methods with a new parametric representation.
The paper tackles the problem of reconstructing shape point clouds from sparse planar cross-sections by introducing a learnable approach using generative modeling, achieving adaptive reconstruction that reduces dependence on the number of input cross-sections.
In this work, we present a novel approach for reconstructing shape point clouds using planar sparse cross-sections with the help of generative modeling. We present unique challenges pertaining to the representation and reconstruction in this problem setting. Most methods in the classical literature lack the ability to generalize based on object class and employ complex mathematical machinery to reconstruct reliable surfaces. We present a simple learnable approach to generate a large number of points from a small number of input cross-sections over a large dataset. We use a compact parametric polyline representation using adaptive splitting to represent the cross-sections and perform learning using a Graph Neural Network to reconstruct the underlying shape in an adaptive manner reducing the dependence on the number of cross-sections provided.