用文本图像生成参数化3D模型，文件小质量高。
A 3D Generation Framework from Cross Modality to Parameterized Primitive
- 基于参数化基本体生成3D模型，自动识别形状特征并替换。
- 生成模型表面光滑，存储仅需约6KB，Chamfer Distance达0.003092。
- 适合快速构建简单3D模型，尤其适用于低存储场景。
近年来，基于AI的3D模型生成利用跨模态信息取得进展，但生成表面平滑且存储开销小的模型仍是挑战。本文提出一种多阶段框架，通过文本和图像输入生成由参数化基本体组成的3D模型。该框架提出一种基于参数化基本体的生成算法，可识别模型构成元素的形状特征，并以高质量表面的参数化基本体进行替换。同时，设计了对应的模型存储方法，仅保留参数即可保持原始表面质量。在虚拟场景与真实场景数据集上的实验表明，该方法在保持高精度的同时显著降低存储需求：达到Chamfer Distance 0.003092、VIoU 0.545、F1-Score 0.9139、NC 0.8369，且参数文件大小约为6KB。该方法特别适用于简单3D模型的快速原型设计。
原文摘要 · Abstract (English)
Recent advancements in AI-driven 3D model generation have leveraged cross modality, yet generating models with smooth surfaces and minimizing storage overhead remain challenges. This paper introduces a novel multi-stage framework for generating 3D models composed of parameterized primitives, guided by textual and image inputs. In the framework, A model generation algorithm based on parameterized primitives, is proposed, which can identifies the shape features of the model constituent elements, and replace the elements with parameterized primitives with high quality surface. In addition, a corresponding model storage method is proposed, it can ensure the original surface quality of the model, while retaining only the parameters of parameterized primitives. Experiments on virtual scene dataset and real scene dataset demonstrate the effectiveness of our method, achieving a Chamfer Distance of 0.003092, a VIoU of 0.545, a F1-Score of 0.9139 and a NC of 0.8369, with primitive parameter files approximately 6KB in size. Our approach is particularly suitable for rapid prototyping of simple models.
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