11-30, 09:50–10:20 (Europe/Amsterdam), Bohr
We have done innovative research on the prediction of changes within 3D-shapes over time, a subject with limited prior exploration due to the scarcity of publicly available 3D data with temporal dimensions. This presentation aims to address the challenges of transitioning from 2D models to a 3D context. To facilitate audience comprehension, we will illustrate our points using practical examples.
Target audience
This presentation is designed for data scientists and engineers. It serves as an informative exploration of a relatively uncharted area and encourages attendees to consider compelling applications for 3D forecasting. While no prior knowledge of 3D modeling is necessary to appreciate this talk, a foundational understanding of Python and neural networks is recommended.
Description
With the rise of 3D printing/scanning, a significant increase in available 3D datasets has taken place. However, most 3D research focusses on classification, recognition or shape analysis of 3D objects. For the past 1,5 year we have researched the subject of predicting growth of natural growing 3D objects using machine learning techniques. During this talk we would like to share our findings and the challenges we encountered when working on this research. The presentation will be structured as follows:
- Introduction and use case (5 minutes)
- Basic 3D-techniques using open3d package (5 minutes)
- Solution method (10 minutes)
- Summary and future research (5 minutes)
- Q&A (5 minutes)
No previous knowledge expected
For the past 5 years I have been working as a Data Science consultant at Pipple. Since Pipple is active in multiple different sectors, I have had the opportunity to do many different projects. During this time I learned that I enjoy logistic projects the most, since they are often complex and require customized solutions every time. However, the most challenging project I did was not in logistics, but in the field of 3D modelling using Python. I would love to tell you more about this project and our solution method during the 2023 edition of Pydata Eindhoven.
