sketch2symm: Symmetry-aware sketch-to-shape generation via semantic bridging
This addresses the challenge of sketch-based 3D reconstruction for applications in computer graphics and design, representing an incremental improvement over prior methods.
The paper tackled the problem of generating 3D shapes from sparse and abstract sketches by proposing Sketch2Symm, a two-stage method that uses semantic bridging and symmetry constraints, resulting in superior performance on metrics like Chamfer Distance, Earth Mover's Distance, and F-Score compared to existing methods.
Sketch-based 3D reconstruction remains a challenging task due to the abstract and sparse nature of sketch inputs, which often lack sufficient semantic and geometric information. To address this, we propose Sketch2Symm, a two-stage generation method that produces geometrically consistent 3D shapes from sketches. Our approach introduces semantic bridging via sketch-to-image translation to enrich sparse sketch representations, and incorporates symmetry constraints as geometric priors to leverage the structural regularity commonly found in everyday objects. Experiments on mainstream sketch datasets demonstrate that our method achieves superior performance compared to existing sketch-based reconstruction methods in terms of Chamfer Distance, Earth Mover's Distance, and F-Score, verifying the effectiveness of the proposed semantic bridging and symmetry-aware design.