# Deep3DSketch+: Obtaining Customized 3D Model by Single Free-Hand Sketch through Deep Learning

arXiv: https://arxiv.org/abs/2310.18609 · HTML: https://mlsanity.com/paper/2310.18609/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2023.

## Built on by (most cited first, 4 of 4)

- [Reasoning3D -- Grounding and Reasoning in 3D: Fine-Grained Zero-Shot Open-Vocabulary 3D Reasoning Part Segmentation via Large Vision-Language Models](https://mlsanity.com/paper/2405.19326/) (2024)
- [Magic3DSketch: Create Colorful 3D Models From Sketch-Based 3D Modeling Guided by Text and Language-Image Pre-Training](https://mlsanity.com/paper/2407.19225/) (2024)
- [From Air to Wear: Personalized 3D Digital Fashion with AR/VR Immersive 3D Sketching](https://mlsanity.com/paper/2505.09998/) (2025)
- [Breaking the Box: Enhancing Remote Sensing Image Segmentation with Freehand Sketches](https://mlsanity.com/paper/2503.12191/) (2025)

## Abstract (arXiv)

As 3D models become critical in today's manufacturing and product design, conventional 3D modeling approaches based on Computer-Aided Design (CAD) are labor-intensive, time-consuming, and have high demands on the creators. This work aims to introduce an alternative approach to 3D modeling by utilizing free-hand sketches to obtain desired 3D models. We introduce Deep3DSketch+, which is a deep-learning algorithm that takes the input of a single free-hand sketch and produces a complete and high-fidelity model that matches the sketch input. The neural network has view- and structural-awareness enabled by a Shape Discriminator (SD) and a Stroke Enhancement Module (SEM), which overcomes the limitations of sparsity and ambiguity of the sketches. The network design also brings high robustness to partial sketch input in industrial applications.Our approach has undergone extensive experiments, demonstrating its state-of-the-art (SOTA) performance on both synthetic and real-world datasets. These results validate the effectiveness and superiority of our method compared to existing techniques. We have demonstrated the conversion of free-hand sketches into physical 3D objects using additive manufacturing. We believe that our approach has the potential to accelerate product design and democratize customized manufacturing.