Revisiting CAD Model Generation by Learning Raster Sketch

The integration of deep generative networks into generating Computer-Aided Design (CAD) models has garnered increasing attention over recent years. Traditional methods often rely on discrete sequences of parametric line/curve segments to represent sketches. Differently, we introduce RECAD, a novel framework that generates Raster sketches and 3D E xtrusions for CAD models. Representing sketches as raster images offers several advantages over discrete sequences: 1) it breaks the limitations on the types and numbers of lines/curves, providing enhanced geometric representation capabilities; 2) it enables interpolation within a continuous latent space; and 3) it allows for more intuitive user control over the output. Technically, RECAD employs two diffusion networks: the first network generates extrusion boxes conditioned on the number and types of extrusions, while the second network produces sketch images conditioned on these extrusion boxes. By combining these two networks, RECAD effectively generates sketch-and-extrude CAD models, offering a more robust and intuitive approach to CAD model generation. Experimental results indicate that RECAD achieves strong performance in unconditional generation, while also demonstrating effectiveness in conditional generation and output editing. Introduction The digital genesis of modern artifacts, from everyday consumer products to complex industrial machinery, is now deeply intertwined with Computer-Aided Design (CAD) systems. Central to many CAD workflows is sketch-based modeling, where 2D sketches imbued with geometric constraints and design intent are transformed into intricate 3D models through a series of feature-based modeling operations, ultimately giving rise to complex assemblies. Among these feature-based modeling operations, extrusion is the most prevalent, allowing designers to generate 3D shapes by extending 2D sketches along a defined path.