IGNF/myria3d
Myria3D: Aerial Lidar HD Semantic Segmentation with Deep Learning
Myria3D helps mapping agencies and urban planners automatically classify features in detailed aerial Lidar scans. You input high-density aerial Lidar point clouds, and it outputs a segmented point cloud where elements like ground, buildings, vegetation, and vehicles are clearly identified. This is useful for professionals working on large-scale 3D mapping projects, especially those dealing with extensive geographical areas.
282 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to precisely categorize different objects and terrain types from vast aerial Lidar datasets for environmental monitoring or urban development.
Not ideal if you're working with general 3D scanning data from sources other than aerial Lidar, or if your primary need is for a highly customizable deep learning framework for various 3D segmentation tasks.
Stars
282
Forks
34
Language
Python
License
BSD-3-Clause
Category
Last pushed
Jun 10, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/IGNF/myria3d"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
PRBonn/lidar-bonnetal
Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving
PRBonn/semantic-kitti-api
SemanticKITTI API for visualizing dataset, processing data, and evaluating results.
venkatasivanaga/FuelDeep3D
R package for LiDAR point-cloud processing and deep-learning inference for 3D fuel mapping.
RWTH-E3D/ifcnet-models
Code for the EG-ICE 2021 Paper "IFCNet: A Benchmark Dataset for IFC Entity Classification"
ika-rwth-aachen/PCLSegmentation
Tensorflow 2.9 Pipeline for Semantic Point Cloud Segmentation with SqueezeSeqV2, Darknet21 and Darknet53.