A 3D Generation Framework from Cross Modality to Parameterized Primitive
It addresses storage and surface quality issues in 3D model generation for rapid prototyping of simple models, representing an incremental improvement.
This paper tackles the problem of generating 3D models with smooth surfaces and minimal storage overhead by introducing a multi-stage framework that uses parameterized primitives guided by textual and image inputs, achieving metrics like a Chamfer Distance of 0.003092 and storage of approximately 6KB per model.
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.