Summary of Biked++: a Multimodal Dataset Of 1.4 Million Bicycle Image and Parametric Cad Designs, by Lyle Regenwetter et al.
BIKED++: A Multimodal Dataset of 1.4 Million Bicycle Image and Parametric CAD Designs
by Lyle Regenwetter, Yazan Abu Obaideh, Amin Heyrani Nobari, Faez Ahmed
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a publicly available dataset of 1.4 million procedurally-generated bicycle designs, represented in JSON files and rasterized images. The dataset is generated using a rendering engine that leverages BikeCAD software to produce vector graphics from parametric designs. This paper also introduces the rendering engine, which is made public alongside the dataset. The primary motivation for this dataset is to train cross-modal predictive models between parametric and image-based design representations. For instance, the authors demonstrate the ability to train a model that accurately estimates Contrastive Language-Image Pretraining (CLIP) embeddings from a parametric representation directly. This enables the establishment of similarity relations between parametric bicycle designs and text strings or reference images. The trained predictive models are also made public. This dataset joins the BIKED dataset family, which includes thousands of mixed-representation human-designed bicycle models and several datasets quantifying design performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shares a big collection of bike designs that can be used to teach computers how to recognize patterns between words and pictures. The data is special because it’s made up of 1.4 million different bike designs, each described in a special code (JSON) and as an image. To make these designs, the researchers used a computer program called BikeCAD and another tool that can turn designs into images. They want to use this data to train computers to understand how words and pictures are related. For example, they showed that a computer can learn to recognize patterns between bike designs and words or pictures. This helps us understand how machines can learn from visual and written information. |
Keywords
* Artificial intelligence * Pretraining
