ethz-asl/autolabel
A project for computing high-quality ground truth training examples for RGB-D data.
This project helps researchers and engineers working with 3D scene understanding by automatically generating high-quality ground truth labels for RGB-D (color and depth) images. It takes raw color and depth frames as input and produces semantically segmented scenes and precise camera poses, which are essential for training AI models in robotics and computer vision. Users include specialists in neural implicit feature fields, scene reconstruction, and autonomous systems development.
No commits in the last 6 months.
Use this if you need to create accurate semantic segmentation masks and 3D scene representations from RGB-D data for training machine learning models in robotics or computer vision.
Not ideal if you are looking for a simple, off-the-shelf solution that doesn't require technical setup or if your primary goal is not 3D scene understanding or autolabeling research.
Stars
48
Forks
3
Language
Python
License
MIT
Category
Last pushed
Jun 10, 2023
Commits (30d)
0
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