Curvy: A Parametric Cross-section based Surface Reconstruction

TL;DR

This paper introduces a learnable method using graph neural networks to reconstruct 3D shapes from sparse cross-sections, improving generalization and simplicity over traditional complex methods.

Contribution

It proposes a novel parametric representation and a GNN-based learning approach for shape reconstruction from limited cross-sectional data.

Findings

Effective reconstruction from few cross-sections

Reduced dependence on the number of input slices

Generalizes across object classes

Abstract

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.