Sketch-based 3D reconstruction enables intuitive content creation through freehand drawings, yet generating high-fidelity 3D models from geometrically ambiguous, structurally simplified, and sparse sketches remain challenging. To overcome existing methods’ limitations in sketch-style generalization, contour accuracy, and suboptimal texture effects, we propose an end-to-end framework that generates textured 3D models directly from a single freehand sketch and semantic labels. To address the scarcity of paired freehand sketch training data, we introduce a 3D model-based automated sketch generation method for extracting mesh contours via a 3D mesh-to-sketch pipeline and synthesizing freehand-style sketches employing a Transformer-based stroke generator to construct a paired dataset of hand-drawn sketches and 3D models. Meanwhile, we design a contour constraint mechanism that jointly optimizes projection-space Chamfer distances and elastic metrics, significantly enhancing the reconstruction accuracy of complex geometries. Furthermore, we integrate a semantic-guided texture generation module using Text2Tex with depth-aware diffusion models and dynamic view-optimization strategies, achieving a complete geometry-appearance integrated modeling pipeline. Finally, extensive experimental results demonstrate that our method outperforms existing structural reconstruction and texture synthesis approaches, exhibiting strong generalization capabilities and practical applicability.