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CLC number: TP31

On-line Access: 2024-02-19

Received: 2023-04-30

Revision Accepted: 2024-02-19

Crosschecked: 2023-11-26

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Tianrun CHEN

https://orcid.org/0000-0003-0177-0157

Ying ZANG

https://orcid.org/0000-0002-1361-1500

Lingyun SUN

https://orcid.org/0000-0002-5561-0493

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.1 P.149-159

http://doi.org/10.1631/FITEE.2300314


Deep3DSketch-im: rapid high-fidelity AI 3D model generation by single freehand sketches


Author(s):  Tianrun CHEN, Runlong CAO, Zejian LI, Ying ZANG, Lingyun SUN

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   tianrun.chen@zju.edu.cn, crl1567@163.com, zejianlee@zju.edu.cn, 02750@zjhu.edu.cn, sunly@zju.edu.cn

Key Words:  Content creation, Sketch, Three-dimensional (3D) modeling, 3D reconstruction, Shape from X, Artificial intelligence (AI)


Tianrun CHEN, Runlong CAO, Zejian LI, Ying ZANG, Lingyun SUN. Deep3DSketch-im: rapid high-fidelity AI 3D model generation by single freehand sketches[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(1): 149-159.

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Abstract: 
The rise of artificial intelligence generated content (AIGC) has been remarkable in the language and image fields, but artificial intelligence (AI) generated three-dimensional (3D) models are still under-explored due to their complex nature and lack of training data. The conventional approach of creating 3D content through computer-aided design (CAD) is labor-intensive and requires expertise, making it challenging for novice users. To address this issue, we propose a sketch-based 3D modeling approach, Deep3Dsketch-im, which uses a single freehand sketch for modeling. This is a challenging task due to the sparsity and ambiguity. Deep3Dsketch-im uses a novel data representation called the signed distance field (SDF) to improve the sketch-to-3D model process by incorporating an implicit continuous field instead of voxel or points, and a specially designed neural network that can capture point and local features. Extensive experiments are conducted to demonstrate the effectiveness of the approach, achieving state-of-the-art (SOTA) performance on both synthetic and real datasets. Additionally, users show more satisfaction with results generated by Deep3Dsketch-im, as reported in a user study. We believe that Deep3Dsketch-im has the potential to revolutionize the process of 3D modeling by providing an intuitive and easy-to-use solution for novice users.

Deep3DSketch-im:基于人工智能从单个手绘草图快速生成高保真三维模型

陈天润1,曹润龙3,李泽健2,臧影3,孙凌云1
1浙江大学计算机科学与技术学院,中国杭州市,310027
2浙江大学软件学院,中国杭州市,310027
3湖州师范学院信息工程学院,中国湖州市,313000
摘要:人工智能生成内容(AIGC)在语言和图像领域的崛起值得注意,但由于其复杂性和缺乏训练数据,基于人工智能生成三维模型仍未被充分探索。通过计算机辅助设计(CAD)创建三维内容的传统方法需大量人力和专业知识,这对于新手用户来说具有挑战性。为解决此问题,提出一种基于草图的三维建模方法,名为Deep3DSketch-im,它利用单个手绘草图进行建模。由于草图的稀疏性和模棱两可性,这是一项具有挑战性的任务。Deep3DSketch-im使用一种称作"有符号距离场(SDF)"的新型数据表示,通过将隐式连续场整合至从草图到三维模型的过程,以及一个特别设计的可以捕捉点和局部特征的神经网络,改进从草图到三维模型的过程。进行了大量实验证明该方法的有效性,在合成数据集和真实数据集上均取得更优的性能。此外,用户研究报告显示,用户对Deep3DSketch-im生成的结果更加满意。我们相信,Deep3DSketch-im有潜力通过为新手用户提供直观易用的解决方案来彻底改变三维建模的过程。

关键词:内容创作;草图;三维建模;三维重建;从X到形状;人工智能

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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