3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

TL;DR

This paper introduces a deep multi-view convolutional network approach for reconstructing detailed 3D shapes from 2D sketches, outperforming existing volumetric methods in fidelity and resolution.

Contribution

The authors develop a novel encoder-decoder network that converts sketches into multi-view depth and normal maps, enabling more accurate 3D reconstructions from limited input views.

Findings

Outperforms volumetric networks in shape fidelity

Produces higher resolution surface reconstructions

Better preserves topology and shape structure

Abstract

We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.