  Recent breakthroughs in geometric Deep Learning (DL) and the availability of
large Computer-Aided Design (CAD) datasets have advanced the research on
learning CAD modeling processes and relating them to real objects. In this
context, 3D reverse engineering of CAD models from 3D scans is considered to be
one of the most sought-after goals for the CAD industry. However, recent
efforts assume multiple simplifications limiting the applications in real-world
settings. The SHARP Challenge 2023 aims at pushing the research a step closer
to the real-world scenario of CAD reverse engineering through dedicated
datasets and tracks. In this paper, we define the proposed SHARP 2023 tracks,
describe the provided datasets, and propose a set of baseline methods along
with suitable evaluation metrics to assess the performance of the track
solutions. All proposed datasets along with useful routines and the evaluation
metrics are publicly available.
