BIKED++：包含140万自行车图像与参数化CAD设计的多模态数据集
BIKED++: A Multimodal Dataset of 1.4 Million Bicycle Image and Parametric CAD Designs
AI总结：
本文提出了BIKED++，一个包含140万程序化生成自行车设计的多模态公开数据集，通过渲染引擎将参数化设计转为图像和JSON文件，并展示了其在训练跨模态预测模型（如直接从参数化表示估计CLIP嵌入）方面的应用潜力。
摘要：
本文介绍了一个包含140万个程序化生成自行车设计的公开数据集，这些设计以参数化形式、JSON文件和光栅图像表示。该数据集通过使用渲染引擎创建，该引擎利用BikeCAD软件从参数化设计生成矢量图形。本文讨论了该渲染引擎，并与数据集一同公开发布。尽管该数据集有众多应用，但一个主要动机是需要在参数化和基于图像的设计表示之间训练跨模态预测模型。例如，我们证明了可以训练预测模型直接从参数化表示中准确估计对比语言-图像预训练（CLIP）嵌入。这允许在参数化自行车设计与文本字符串或参考图像之间建立相似性关系。训练好的预测模型也已公开。该数据集加入了BIKED数据集家族，该家族包括数千个混合表示的人类设计自行车模型和几个量化设计性能的数据集。代码和数据集可在以下网址找到：https://github.com/Lyleregenwetter/BIKED_multimodal/tree/main
英文摘要：
This paper introduces a public dataset of 1.4 million procedurally-generated bicycle designs represented parametrically, as JSON files, and as rasterized images. The dataset is created through the use of a rendering engine which harnesses the BikeCAD software to generate vector graphics from parametric designs. This rendering engine is discussed in the paper and also released publicly alongside the dataset. Though this dataset has numerous applications, a principal motivation is the need to train cross-modal predictive models between parametric and image-based design representations. For example, we demonstrate that a predictive model can be trained to accurately estimate Contrastive Language-Image Pretraining (CLIP) embeddings from a parametric representation directly. This allows similarity relations to be established between parametric bicycle designs and text strings or reference images. Trained predictive models are also made public. The dataset joins the BIKED dataset family which includes thousands of mixed-representation human-designed bicycle models and several datasets quantifying design performance. The code and dataset can be found at: https://github.com/Lyleregenwetter/BIKED_multimodal/tree/main
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
