VL3D++ technical documentation
The documentation is the main source to understand the framework, its components, pipelines, examples and paradigms (Deep Learning 3D and Active Learning).
VIEW OFFICIAL DOCUMENTATIONSource code and usage guide (GitLab)
The repository contains the full framework, JSON configuration, installation, tests, examples and GPU considerations.
VIEW REPOSITORY AND EXECUTIONPeer-reviewed articles and methodological foundations that support the viability and technology of the VL3D++ framework
Datasets and open data used in use cases
See the framework working in different scenarios
The documentation includes runnable examples showing the complete pipeline:
raw cloud → prediction → errors/uncertainty → metrics.
Deep Learning 3D
VL3D++ works directly on 3D geometry: local neighbourhoods, receptive fields, local prediction and global reconstruction.
LEARN MOREActive Learning
The framework enables cycles where the model identifies areas of uncertainty and the expert only corrects where it adds value.
LEARN MORETo understand neural networks visually
If you need a well-made introductory explanation to understand the concept of a neural network (without diving into papers), this resource is excellent:
WHAT IS A NEURAL NETWORK?Licences and attribution
The documentation of VirtualLearn3D++ is published under the CC BY 4.0 licence (attribution required).
The VL3D++ framework is published under the MIT licence.
On the project website, any external figure or resource must keep its corresponding attribution.
