  We introduce DeepJoin, an automated approach to generate high-resolution
repairs for fractured shapes using deep neural networks. Existing approaches to
perform automated shape repair operate exclusively on symmetric objects,
require a complete proxy shape, or predict restoration shapes using
low-resolution voxels which are too coarse for physical repair. We generate a
high-resolution restoration shape by inferring a corresponding complete shape
and a break surface from an input fractured shape. We present a novel implicit
shape representation for fractured shape repair that combines the occupancy
function, signed distance function, and normal field. We demonstrate repairs
using our approach for synthetically fractured objects from ShapeNet, 3D scans
from the Google Scanned Objects dataset, objects in the style of ancient Greek
pottery from the QP Cultural Heritage dataset, and real fractured objects. We
outperform three baseline approaches in terms of chamfer distance and normal
consistency. Unlike existing approaches and restorations using subtraction,
DeepJoin restorations do not exhibit surface artifacts and join closely to the
fractured region of the fractured shape. Our code is available at:
https://github.com/Terascale-All-sensing-Research-Studio/DeepJoin.
