Awesome 3D Machine Learning
A curated repository of resources, datasets, and research papers for 3D machine learning, covering computer vision, graphics, and deep learning.
What is Awesome 3D Machine Learning?
3D Machine Learning is a curated GitHub repository that aggregates resources for the interdisciplinary field combining computer vision, computer graphics, and machine learning. It provides structured access to courses, datasets, and research papers focused on 3D data analysis, synthesis, and understanding. The repository serves as a study notes collection and triaging system for new research in 3D representations like point clouds, meshes, and volumetric data.
Researchers, graduate students, and practitioners working in 3D computer vision, geometric deep learning, and 3D data analysis who need organized access to academic resources and datasets.
It saves significant time by centralizing scattered academic resources into a well-structured repository with clear categorization by 3D representation types and research tasks, facilitating faster literature review and experimentation.
Overview
A resource repository for 3D machine learning
Use Cases
Best For
- Finding benchmark datasets for 3D object classification and segmentation tasks
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