SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point Clouds and 3D Scans. Overview, Datasets, Metrics, and Baselines.

Dimitrios Mallis, Ali Sk Aziz, Elona Dupont, Kseniya Cherenkova, Ahmet Serdar Karadeniz, Mohammad Sadil Khan, Anis Kacem, Gleb Gusev, Djamila Aouada; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 1786-1795

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 continue to make multiple simplifying assumptions and applications in real-world settings remain limited. 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 track solutions. All proposed datasets along with useful routines and the evaluation metrics are publicly available.