Sketch-to-Design: Context-based Part Assembly

Abstract

Designing 3D objects from scratch is difficult, especially when the user intent is fuzzy without a clear target form. In the spirit of modeling-by-example, we facilitate design by providing reference and inspiration from existing model contexts. We rethink model design as navigating through different possible combinations of part assemblies based on a large collection of pre-segmented 3D models. We propose an interactive sketch-to-design system, where the user sketches prominent features of parts to combine. The sketched strokes are analyzed individually and in context with the other parts to generate relevant shape suggestions via a design gallery interface. As the session progresses and more parts get selected, contextual cues becomes increasingly dominant and the system quickly converges to a final design. As a key enabler, we use pre-learned part-based contextual information to allow the user to quickly explore different combinations of parts. Our experiments demonstrate the effectiveness of our approach for efficiently designing new variations from existing shapes.

11 pages; Executable: see project webpage

Cited by in corpus (5)

• 3D Sketching using Multi-View Deep Volumetric Prediction
• DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
• SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks
• Modeling and Correspondence of Topologically Complex 3D Shapes
• RISA-Net: Rotation-Invariant Structure-Aware Network for Fine-Grained 3D Shape Retrieval