This repository contains a Jupyter Notebook tutorial on the Octree Transformer for Autoregressive Shape Generation, based on the research paper by Gregor Kobsik, Moritz Ibing, and Leif Kobbelt at RWTH Aachen University. The tutorial provides a hands-on introduction to various aspects of the Octree data structure, its application in shape representation and generation, the Octree Transformer architecture, and compression techniques for managing the complexity of 3D tasks.

Octree Trasnformer: Autoregressive 3D Shape Generation

• Octree Data Structure and Its Applications: Gain insights into the Octree data structure and its significance in representing and generating complex shapes efficiently. Understand the advantages it offers in terms of compact representation and processing speed.

• Octree Transformer Architecture: Explore the Octree Transformer, a novel approach that employs the Transformer architecture for processing Octree-based inputs. Learn how the Transformer's attention mechanisms can be leveraged to handle hierarchical structures effectively.

• Compression Schemes for Input and Output: Since 3D tasks often involve substantial complexity, the tutorial covers essential compression techniques for managing information. Discover convolution compression and deconvolution techniques for efficient handling of input and output data, ensuring computational feasibility.

1. Introduction to Octree Data Structure

   • Understanding the Octree data structure and its role in shape representation.

2. Octree Transformer Architecture

   • Detailed exploration of the Octree Transformer, explaining its components and mechanisms.

3. Compression Techniques

   • Learn about compression methods crucial for dealing with the complexity of 3D tasks.

4. Hands-On Training and Shape Generation

   • Step-by-step instructions on training the model and generating complex shapes using the Octree Transformer.

To dive into the tutorial and experience the power of Octree Transformer for autoregressive shape generation, follow these steps:

1. Clone this repository to your local machine:

   git clone https://github.com/lukasVierling/OctreeTransformer-tutorial.git

2. Install the required dependencies by setting up a Python environment:

   conda create -n octree-transformer-env python=3.9
   conda activate octree-transformer-env
   pip install -r requirements.txt

3. Follow the notebook's instructions to learn about Octree Transformer and experiment with autoregressive shape generation.

This tutorial is inspired by the work of Gregor Kobsik, Moritz Ibing, and Leif Kobbelt at RWTH Aachen University. We extend our gratitude to them for their research contributions.

Feel free to explore, learn, and contribute to this repository. We hope this tutorial enhances your understanding of the Octree Transformer and its applications.

For inquiries and feedback, please contact [email protected]. Your input is valuable to us!