As I delved into the world of 3D deep learning, I realized that finding organized and comprehensive resources can be a daunting task. I’m not alone in this struggle, and I’m sure many of you have faced similar challenges. That’s why I decided to share some valuable resources that can help you get started with 3D deep learning.
Before we dive in, it’s essential to understand that 3D deep learning is a complex and multidisciplinary field that combines computer vision, machine learning, and 3D geometry. To master it, you’ll need a solid grasp of the fundamentals, including linear algebra, calculus, and programming skills.
Here are some top resources to help you get started:
* Online Courses:
– 3D Deep Learning by University of California, Berkeley on edX
– Deep Learning for Computer Vision by Stanford University on Stanford Online
* Tutorials and Guides:
– 3D Deep Learning Tutorial by PyTorch
– A Gentle Introduction to 3D Deep Learning by Towards Data Science
* Research Papers:
– DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
– Occupancy Networks: A New Representation for 3D Reconstruction
* Communities and Forums:
– Kaggle 3D Deep Learning Competition
– Reddit’s r/MachineLearning and r/ComputerVision communities
These resources will provide a solid foundation for your 3D deep learning journey. Remember to practice, experiment, and stay updated with the latest advancements in the field.
What are some of your favorite resources for 3D deep learning? Share them with us in the comments below!