A Sketch-to-3D Shape Generation Method Based on Retrieval Enhancement and Adapter Guidance

Aiming at the problems of missing accuracy and details in 3D shape generation caused by sparse sketch information and insufficient semantic expression, this paper proposes a sketch-to-3D shape generation method based on retrieval enhancement and adapter guidance, mainly including two parts: sketch feature enhancement based on retrieval and shape control generation guided by adapter. The former enhances the semantic information of the sketch by retrieving similar color images, effectively improving the generation accuracy and details. The latter proposes a sketch—shape adapter, combined with the cross-attention mechanism, to achieve the deep integration of sketch features and the diffusion process, significantly im-proving the consistency between the generated shape and the overall structure of the sketch. Experiments on the ShapeNet-Synthetic and ShapeNet-Sketch subsets show that the proposed method achieves the op-timal performance in both the voxel Intersection over Union (IoU) and the Chamfer Distance (CD) indica-tors: The average IoU reaches 67.18%, which is 1.05 percentage points higher than that of the existing op-timal method; The average CD was 0.003 97, which was 0.007 53 lower than that of the optimal method. The experimental results verifies the effectiveness of the retrieval enhancement mechanism and the sketch-shape adapter in improving the overall structural consistency and detail restoration, providing a new technical path for the sketch-driven 3D modeling task.