-
[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Karadeniz_2025_WACV, author = {Karadeniz, Ahmet Serdar and Mallis, Dimitrios and Mejri, Nesryne and Cherenkova, Kseniya and Kacem, Anis and Aouada, Djamila}, title = {PICASSO: A Feed-Forward Framework for Parametric Inference of CAD Sketches via Rendering Self-Supervision}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {6475-6484} }
PICASSO: A Feed-Forward Framework for Parametric Inference of CAD Sketches via Rendering Self-Supervision
Abstract
This work introduces PICASSO a framework for the parameterization of 2D CAD sketches from hand-drawn and precise sketch images. PICASSO converts a given CAD sketch image into parametric primitives that can be seamlessly integrated into CAD software. Our framework leverages rendering self-supervision to enable the pre-training of a CAD sketch parameterization network using sketch renderings only thereby eliminating the need for corresponding CAD parameterization. Thus we significantly reduce reliance on parameter-level annotations which are often unavailable particularly for hand-drawn sketches. The two primary components of PICASSO are (1) a Sketch Parameterization Network (SPN) that predicts a series of parametric primitives from CAD sketch images and (2) a Sketch Rendering Network (SRN) that renders parametric CAD sketches in a differentiable manner and facilitates the computation of a rendering (image-level) loss for self-supervision. We demonstrate that the proposed PICASSO can achieve reasonable performance even when finetuned with only a small number of parametric CAD sketches. Extensive evaluation on the widely used SketchGraphs and CAD as Language datasets validates the effectiveness of the proposed approach on zero- and few-shot learning scenarios.
Related Material