3D-Machine-Learning

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3D-Machine-Learning is an Open Source research and education tool that serves as a resource repository for 3D machine learning, fusing computer vision, computer graphics, and machine learning.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is 3D-Machine-Learning?

3D-Machine-Learning is a comprehensive GitHub repository dedicated to curating resources in the interdisciplinary field of 3D machine learning. It functions as a dynamic study note and a platform for triaging new research papers, covering topics from 3D pose estimation to scene synthesis. The repository categorizes resources using distinct icons for different 3D representations like multi-view images, volumetric data, point clouds, polygonal meshes, and primitive-based models. It also provides links to related academic papers through Connected Papers for visualizing research relationships. The project encourages community involvement through pull requests and a Slack workspace to foster collaboration and knowledge sharing among researchers and enthusiasts.

Best used for

Ideal for professors who need to stay updated on the latest research in 3D machine learning, find relevant datasets and 3D models for their work, and foster collaboration within the academic community. Especially valuable for those looking for a centralized, community-driven resource.

Common actions

research 3D machine learning

find 3D datasets

explore 3D models

collaborate on research

open-sourceautomated workflowlow-code/no-codedeepfakecollaborationface swapping"AI Agents"workflowsgithub copilot

Capabilities

Key features

Curated research papers
Categorized 3D representations
Datasets and 3D models
Community collaboration
Contribution via pull requests

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of 3D representations are covered in the repository?

The repository uses icons to differentiate various 3D representations, including multi-view images, volumetric data, point clouds, polygonal meshes, and primitive-based models. This helps users quickly identify the type of 3D data associated with each research paper or resource.

How can I contribute to the 3D-Machine-Learning repository?

You can contribute to the repository by adding new content through pull requests on GitHub. Alternatively, you can open an issue to suggest new research papers, datasets, or other resources that you believe would be valuable additions to the collection.

Is there a community for discussing 3D machine learning topics related to this repository?

Yes, the project has created a Slack workspace to facilitate discussions, knowledge sharing, and collaborations among individuals interested in 3D machine learning. You can join the community via the provided link in the repository's README.

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Subcategories trending in Research & Education

Academic Research Study Assistants Knowledge Management Scientific Computing Summarization Language Learning

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