Hello, dev.to community! 👋
It's time for an update on our ChickenVision🐔👀 project! We've been exploring different technologies and libraries for 3D data processing and visualization to enhance our augmented reality app. Today, we're sharing our insights on Open3D and its applicability for frame rendering in our project.
If you missed our previous posts, check out our introduction to ChickenVision, YOLOv7 research update, UI/UX research & design update, and Detectron2 research update.
Open3D Frame Rendering 🖼️
Open3D is a versatile library for 3D data processing and visualization. In ChickenVision🐔👀, we've used Open3D to manipulate meshes – specifically, to translate and rotate them in Cartesian coordinates.
Advantages 👍
- Seamless integration with Python
- A rich set of features for 3D data processing and visualization
- Interactive visualizer for observing 3D models, meshes, and point clouds in real-time
- Open-source nature promotes transparency and collaboration
Disadvantages 👎
- Steep learning curve
- Limited documentation
- Performance-related issues with some functions
For more details on Open3D's installation and usage in our project, check out our Open3D research report.
A Fond Farewell 👋
Sadly, we couldn't complete the project in time for the hackathon because our team members faced the challenges of finals. We plan to continue working on ChickenVision🐔👀, but we won't be submitting it for the #githubhack23 competition.
We'd like to express our gratitude to you, our readers, for following our progress and supporting us throughout this journey.
Until next time, keep clucking on! 🐔🚀
Warm regards,
The Pavoculus team 🦃
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